Habilitační řízení Uchazeč:
Ing. Miroslav Vořechovský, Ph.D.
Obor:
Teorie a konstrukce staveb
Datum podání žádosti: 7. 2. 2007 Datum konání 18. 5. 2007 habilitační přednášky: Datum obhajoby před VR FAST:
6. 6. 2007
Složení habilitační komise Předseda:
Prof. Ing. Drahomír Novák, DrSc., FAST VUT v Brně
Člen:
Prof. RNDr. Ing. Petr Štěpánek, CSc., FAST VUT v Brně
Člen:
Prof. Ing. Zdeněk Bittnar, DrSc., FSv ČVUT v Praze
Člen:
Prof. Ing. Jan L. Vítek, CSc., Metrostav, FSv ˇCVUT v Praze
Člen:
Doc. Ing. Petr Janas, CSc., FAST VŠB-TU Ostrava
Složení komise hodnotící přednášku Předseda:
Prof. RNDr. Zdeněk Chobola, CSc., FAST VUT v Brně
Člen:
Prof. Ing. Drahomír Novák, DrSc., FAST VUT v Brně
Člen:
Prof. Ing. Břetislav Teplý, CSc., FAST VUT v Brně
Oponenti habilitační práce Prof. Ing. Zdeněk Bittnar, DrSc., FSv ČVUT v Praze Prof. Ing. Jan L. Vítek, CSc., Metrostav, FSv ČVUT v Praze Doc. Ing. Petr Kabele, Ph.D., FSv ČVUT v Praze
Abstrakt tezí habilitační přednášky 1
B RNO U NIVERSITY OF T ECHNOLOGY FACULTY OF C IVIL E NGINEERING I NSTITUTE OF S TRUCTURAL M ECHANICS
Ing. Miroslav Voˇrechovsk´y, Ph.D.
Stochastic Computational Mechanics of Quasibrittle Structures Stochastick´a v´ypoˇctov´a mechanika kvazikˇrehk´ych konstrukc´ı
S HORT VERSION OF HABILITATION THESIS
BRNO 2007
KEY WORDS probabilistic-based assessment, failure probability, reliability, statistical analysis, sensitivity analysis, reliability analysis reliability software, software design, design of experiments, adaptive sampling refinement, sampling, Monte Carlo simulation, Latin Hypercube Sampling, random vectors, random fields, theory of extreme values, dependence, copula, correlation, autocorrelation, cross correlation, multivariate random field, combinatorial optimization, simulated annealing, stochastic finite element method size effect, scaling, quasibrittle structures, quasibrittle materials, concrete, multifilament yarn, textile reinforced concrete, fiber bundle models, chain of bundles, delayed activation, slack, twisted yarns, E-Glass, tensile test, nonlinear fracture mechanics, Weibull theory, cohesive crack, weak boundary, damage, crack band model, microplane model, nonlocal damage, dog-bone specimens, quasibrittle failure, quasibrittle fracture, crack initiation, characteristic length, characteristic length, Malpasset dam, dam safety, adaptive finite element method, length scales interaction, statistical size effect ˇ ´ SLOVA KLI´ COV A Spolehlivostn´ı posouzen´ı, pravdˇepodobnost poruchy, spolehlivost, statistick´a anal´yza, citlivost, spolehlivostn´ı software, n´avrh experiment˚u, adaptivn´ı vzorkov´an´ı, Latin Hypercube Sampling, n´ahodn´a pole, teorie extr´emn´ıch hodnot, kombinatorick´a optimalizace, simulovan´e zˇ´ıh´an´ı, stochastick´a metoda koneˇcn´ych prvk˚u vliv velikosti (mˇerˇ´ıtka), kvazikˇrehk´e konstrukce, beton, mnohovl´aknit´e svazky, modely svazku vl´aken, zpoˇzdˇen´a aktivace, neline´arn´ı lomov´a mechanika, kohezivn´ı trhlina, teorie extr´emn´ıch hodnot, Weibullova teorie, simulace n´ahodn´ych pol´ı, charakteristick´a d´elka, adaptivita s´ıt´ı koneˇcn´ych prvk˚u, interakce sˇ a´ lovac´ıch d´elek
Origin´al pr´ace je uloˇzen arch´ıvu oddˇelen´ı pro vˇedu a v´yzkum FAST VUT v Brnˇe c Miroslav Voˇrechovsk´y 2007
ISBN 978-80-214-3423-3 ISSN 1213-418X Typeset by LATEX 2ε
2
CONTENS
Professional Biosketch of the Author
4
1
INTRODUCTION
6
2
AIMS AND STRUCTURE OF THE THESIS
7
3
SIMULATION OF RANDOM VARIABLES 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 LHS: Sampling and statistical correlation . . . . . . . . . . . . . . . 3.3 Stochastic optimization method Simulated Annealing . . . . . . . .
9 9 9 11
4
SIMULATION OF RANDOM FIELDS 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Orthogonal transformation of covariance matrix . . . . . . . . . . 4.3 Novel technique for simulation of cross correlated non-Gaussian random fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Error assessment of random field simulation . . . . . . . . . . . .
. .
14 14 14
. .
15 17
5 6
MULTI-FILAMENT YARNS AND TRC INTERACTION OF ENERGETIC AND STATISTICAL SIZE EFFECTS IN QUASIBRITTLE FAILURE 6.1 Case study on dog-bone specimens . . . . . . . . . . . . . . . . . . 6.2 Introduction and development of the new size effect law . . . . . . . 6.3 Superimposition of deterministic and statistical size effects . . . . .
21
23 24 27 29
7
PROBABILISTIC ADAPTIVE FINITE ELEMENT METHOD
32
8
FREET SOFTWARE
34
9
CONCLUSIONS
34
Summary in Czech
40
3
Professional Biosketch of the Author
Personal data Miroslav Voˇrechovsk´y Institute of Structural Mechanics Faculty of Civil Engineering Brno University of Technology Veveˇr´ı 95 635 00 Brno Czech Republic Tel.: (+ 420) 5 41 14 73 70 E- Mail:
[email protected] http://www.fce.vutbr.cz/STM/vorechovsky.m Born on February 07, 1977 in Brno, Czech Republic single, Czech nationality
Education 09/1995–06/2000 Master’s degree “Ing.” (equiv C-Eng or MSc) Brno University of Technology, Brno, Czech Republic. Diploma thesis: “On reliability calculations of problems of nonlinear continuum mechanics” (in Czech) 09/2000–09/2004 Doctoral degree “Ph.D.”, Brno University of Technology, Brno, Czech Republic. Dissertation: “Stochastic Fracture Mechanics and Size Effect” (in English)
Awards, honors and scholarships 6/2000: Award of Dean, Diploma thesis Award, and Rector’s Prize, all three at the Brno University of Technology; 01/2001: Doctoral scholarship, Hl´avka foundation, Prague; 05/2001: Electricit´e de France, Conference Stipend of Excellence, FraMCoS, France, Paris, 2001; 04/2002: Award of Josef Hl´avka foundation, Prague; 05/2002: Travel stipend for conference in Scotland, Dundee; 9/2002–6/2003: Preciosa scholarship; 09/2002: A good paper award at the 4th International Ph.D. Symposium in Civil Engineering, Munich, Germany; 05/2003: M.I.T. Young Researcher Fellowship (award), 2nd M.I.T. Conference, Boston, USA; 06/2003: Cerra award, ICASP 9 conference, San Francisco, USA 09/2003–03/2004: Fulbright Doctoral Fellowship, Northwestern University, Evanston, USA; 09/2004: Rector’s Prize, Brno University of Technology; 02/2005: Trimo Research Award 2005, Trimo corporation, Slovenia; 2006: Professional CV record in Marquis “Who’s who in the World 2007”, 24th Ed.
Specialization, research interests Nonlinear fracture mechanics with focus on stochastic aspects. Size effects, scaling in structures. Behavior of fibers, yarns and fiber-reinforced composites. Efficient methods of reliability engineering, mathematical statistics (random variables, random fields and processes, extreme value theories) connected with nonlinear fracture mechanics methods (research on behavior of quasibrittle materials/structures). Stochastic optimization techniques, structural safety and reliability, stochastic computational mechanics, Monte Carlo simulation techniques, genetic algorithms. Programming and software development.
Memberships, activities 09/2000: member of FR A MC O S society (FRacture Mech. of Concrete Structures); since 01/2004: member of ASCE (American Society of Civil Engineers); since 02/2005: member of the Czech Society of Mechanics; 1997–1999: Academic senate and Branch council (Structures and transportation constructions); 09/2000: Member of Local Organizing Committee: Workshop 3RE, Institute of Structural mechanics TUB together with Institute of Structural Mechanics, Weimar, Germany; 02/2001: Chairman of section, Member of Local Organizing Committee: Brno University of Technology, Faculty of Civil Engineering, 3rd Scientific PhD international workshop; 06/2005: Member of Local Organizing Committee: 2nd International symposium Nontraditional cement & concrete.
4
Brno University of Technology, Brno, Czech Republic; 07/2006: Chairman of session “Size effects”: Alexandroupolis, Greece, 16th European Conference of Fracture; 01/2007: Member of Organizing Committee: Brno University of Technology, Faculty of Civil Engineering, Juniorstav (Scientific PhD international workshop).
International cooperation since 2003
Prof. Z. P. Baˇzant Northwestern University, Evanston, Illinois, USA: stochastic fracture mechanics of quasibrittle materials
since 2003
Dr. R. Chudoba, Technical University of Aachen (RWTH Aachen), Germany: stochastic fracture mechanics of textile reinforced concrete, computational mechanics
Teaching experience since 09/2000
Institute of Structural Mechanics, Faculty of Civil Engineering, Brno University of Technology, Brno, Czech Republic: 0D1 Structural Mechanics I (ex.); 0D2 Elasticity and Plasticity (ex.); 0D3 Structural Analysis (1) (ex.); BD01 Fundamentals of Structural Mechanics (ex.); 5D4 Selected Topics of Structural Mechanics II (lect. & ex.); 5D5 Reliability of building structures (lect. & ex.); 0D7 Nonlinear Mechanics (lect. & ex.); 6D0 Nonlinear Mechanics (lect. & ex.); BD02 Elasticity and Plasticity (Eng. lect.); D31 Reliability of Building Structures (1) (Eng. lect. & ex.); CD04 Theory of Structural Reliability (lect. & ex.); CD06 Theory of Structural Reliability (lect. & ex.); CD57 Reliability and theory of material damage (garant); CD59 Reliability and theory of material damage (garant); ZD51 Structural reliability (lect. & ex.);
Participation in Research projects Responsible inversigator 9/2004 – 6/2007: “Statistical aspects of structural size effect” (key person) Kl´ıcˇ ov´a osoba. ˇ Project MSMT: 1K04110; 1/2006 – 12/2008: “Probabilistic nonlinear finite element method with ˇ 103/06/P086; 2007: FRVSˇ 2164 (TypAa): “Experimantal lab of the Institute h-adaptivity”. GACR of Structural Mechanics” (capital investment). other National projects 12x, International of foreing projects: 5x.
Professional stays, visiting positions 07/2001–08/2001 summer school (two-week course) Advanced Studies in Structural Engineering and CAE, 9th European Summer Academy 2001. Certificate of Attendance from Bauhaus University Weimar, Germany, Civil & Structural Engineering. Passed final oral examination with the mark A (ECTS Grade) and therefore credited with 7 ETCS credits 08/2001–09/2001 Professional 6-week training IAESTE, Croatia, Zagreb. The Dalekovod Company. Focus on Antennae towers, transmission lines (steel structures); Foundations of structures (concrete structures and soil mechanics) 01/2003–03/2003 Visiting research position in Aachen, Germany. Invited lectures. Cooperation on development of methodology and software for consideration of stochastic aspects in failures of Textile Reinforced Concrete 04/2003–05/2003 Visiting research position in Aachen, Germany. Invited lectures. Cooperation on development of methodology and software for consideration of stochastic aspects in failures of Textile Reinforced Concrete 06/2003–07/2003 Visiting research position at Prof. Z.P. Baˇzant: Northwestern University, Evanston, IL, USA 08/2003
Fulbright preacademic training, University of Philadelphia, USA
08/2003–04/2004 Visiting research position (Fulbright scholarship) at Prof. Z.P. Baˇzant: Northwestern University, Evanston, IL, USA (extension supported by NSF grants to Prof. Z.P. Baˇzant) 11/2004–04/2005 Research position in Aachen, Germany. Cooperation on development of methodology and software for consideration of stochastic aspects in failures of concrete structures.
Publications Journal papers
Conf. papers Reports etc.
Journals with IF>1.0: 4x (International journal of Solids and Structures 3x, International journal of Numerical Methods in Engineering 1x); Journals with IF<1.0: 2x (Journal of Engineering Mechanics ASCE 1x, Engineering mechanics 1x). International conference papers: 31; National and Slovak conference papers: 32x. Technical and final reports 9x, Theses 3x, Lecture notes 1x.
5
1
INTRODUCTION
Loading conditions, material properties, geometry and several other parameters often show considerable variability, since they are stochastic quantities in nature. The rational treatment of uncertainties in computational mechanics, based on statistical and probabilistic methods, is object of increasing attention, particularly in recent years. Indeed, proper methods are required for propagating uncertainties from modeling parameters describing the geometry, the material behavior and the applied loading to structural response quantities (engineering demand parameters) used to define the structural performance objectives. These methods need also to be integrated with analysis methodologies already well-known to practitioners, such as the ubiquitous finite element (FE) method. The FE method is widely used and largely accepted in the engineering community at large: researchers, practitioners and codes. Efficient methods for numerical analysis of (reinforced) concrete structures have been the objective of much research during the last few decades, and the main difficulty has been how to best capture material nonlinearity. The aim is to model the complete response of a structure including the crack propagation in the pre-peak, peak and post-peak states. A form of fracture mechanics that can be applied to such kind of fracture analysis has been developed during the last three decades. Recently, commercial finite element programs, using the crack band approach, have become available for this purpose. These tools, however, remain at the deterministic level. On the other hand, the design practice in industry provides motivation mainly for efficient implementation of existing simple material models, solution strategies, discretization and interpretation of results. These topics will naturally remain as the priorities for commercial software developers. But exceptions are nowadays appearing – the interdisciplinary field of stochastic fracture mechanics is now finally infringed on by some advanced software developers, e.g. those of ATENA [8] or DIANA [24]. The salient feature of quasi-brittle materials is a complex size effect on structural strength. Size effect phenomenon manifests itself in form of a strong dependence of a nominal strength (nominal stress at the failure load) on a characteristic dimension (size) of the geometrically similar structures, see Fig. 1. Since the uncertainties and spatial variability is inherently present in nature, the nominal strength has certain variability. The size effect is also characterized/accompanied by the change of the nominal strength variability for different structure sizes. Size effect phenomenon has a great impact on a safe design and assessment of structures. The size effect is not present in current strength theories (either plasticity or elasticity). The problem is that real large structures usually fracture under smaller failure load than laboratory size specimens, see Fig. 1. The reasons for complex reliability treatment of nonlinear fracture mechanics problems can be summarized as follows: (i) Modeling of uncertainties (material, 6
load and environments) in classical statistical sense as random variables or random processes (fields). The possibility to use statistical information from real measurements; (ii) Inconsistency of design to achieve safety using partial safety factors – fundamental problem; (iii) Size effect phenomena.
Nominal strength (logarithmic scale)
Nonlinear fracture mechanics and plasticity theory
Stochastic nonlinear Statistical strength fracture mechanics theories Main focus of the thesis
Laboratory Real specimens concrete structures Characteristic structure size (logarithmic scale)
Fig. 1: Illustration of size effect on nominal strength and the range of structural sizes of interest
2
AIMS AND STRUCTURE OF THE THESIS
The title of the thesis “Stochastic computational mechanics of quasibrittle structures” suggests the attempt to combine both, the advanced tools of fracture (nonlinear) mechanics and stochastic approaches in order to model the complex behavior of real material/structures considering material randomness or variability. The whole focus looks towards complex description and understanding of size effect phenomena. The main attention is devoted to concrete as a main representative of quasibrittle material (such as rock, tough ceramics, snow and ice, etc.) and one of the most important building materials in civil engineering. This thesis is focused mainly on the range of sizes where both phenomena, statistical and deterministic plays a significant role, see the transitional zone in Fig. 1. This transition represents the range with the most difficult structural scaling theory. Aims of chapter 3 are to briefly review simulation methods of Monte Carlo type for efficient structural stochastic assessment and to introduce Latin Hypercube Sampling (LHS) as a technique suitable for an analysis of computationally intensive problems which is typical for a nonlinear FEM analysis. In particular the chapter suggests a new procedure for efficient imposition of statistical correlation among input variables. The technique is robust, efficient and very fast. The following chapter 4 is devoted to efficient simulation of random fields for problems of stochastic continuum mechanics. In particular, the transformation of the original random variables into a set of uncorrelated random variables is presented using an eigenvalue orthogonalization procedure. It is demonstrated that only a few of these uncorrelated variables with largest eigenvalues are sufficient for the accurate representation of the (vector) random field. 7
The next chapter of the thesis is focused on textile reinforced concrete, a new composite material for special purposes. The thesis presents a newly developed micromechanical model which is combined with advances stochastic techniques (random variables and random processes capturing the spatial variability of uncertain parameters). These models are given to context with classical approaches and we proved that there must exist (as opposite to Weibull integral) statistical length scale. It is explained why the nonlocal Weibull integral is not general enough solution for the presented problems. The author has proposed new formulas which are designed based on asymptotic matching for approximation and prediction of the yarn strength under various conditions and for the whole range of yarn lengths. These formulas are compared to available statistical theories of strength of bundles. The detailed analysis of all substantial effect in the context of tensile test of yarn enabled design of practical procedure of testing and evaluation of yarn strength. It is shown how to decompose, analyze and compose partial phenomena present in the yarn tensile test. The following chapter introduces a new approach to stochastic nonlinear analyses of large structures. Standing firmly on the statistical theory of extreme values the text proposes a practical tool for simulation of random scatter (spatial variability) in the context of FEM which is independent of the mesh. In some sense the approach brings similar features to famous crack band model in deterministic computational fracture mechanics [5]. Similarly to the crack band model which is proved to be theoretically correct and compared to cohesive (fictitious) crack model, the developed stochastic crack band model is derived from elaborate theory of ordered statistics and extreme values [10, 12, 11, 25, 7, 40, 27]. The range of applicability (large structures) is explained and it is shown that the model performs well in the size regions, where the combination of NLFEM and simulation of random fields is not useful. This is because in case of large structures the computational demands render the utilization of random fields inapplicable. The feasibility, correctness and predictive power of the approach is shown using numerical examples. The problem of structural scaling in a broad range of sizes is studied in chapter 6. The behavior of general quasibrittle material is shown to be the complex case of behavior covering both the plastic and elastic-brittle behavior on two asymptotic extremes of sizes. The work resulted in the new combined size effect formula for crack initiation problems of quasibrittle failure. The new law covers both the deterministic scaling (characteristic material length) and statistical scaling (autocorrelation length of variable strength) and their interaction over the whole range of sizes. The asymptotic limits are checked with help of deterministic plasticity of the small-size structures and stochastic-brittle behavior (Weibull type) of the large-size structures. A numerical verification of the theoretical consistency with the assumptions is performed with the practical example of Malpasset Dam failure in French Alps [39]. Stochastic simulations presented in the thesis were performed with simulation 8
software developed by the author. The software constitutes the core of computer program FReET presented in chapter 7.
3
SIMULATION OF RANDOM VARIABLES 3.1 Introduction
The aim of statistical and reliability analysis of any computational problem which can be numerically simulated is mainly the estimation of statistical parameters of response variable and/or theoretical failure probability. Pure Monte Carlo simulation cannot be applied for time-consuming problems, as it requires large number of simulations (repetitive calculation of response). Small number of simulations can be used for acceptable accuracy of statistical characteristics of response using stratified sampling technique Latin Hypercube Sampling [19, 14, 1]. Briefly, it is a special type of Monte Carlo numerical simulation which uses the stratification of the theoretical probability distribution functions of input random variables. Stratification with proportional allocation never increases variance compared to IID sampling, and can reduce it. The efficiency of LHS technique was showed first time in work of [19], but only for uncorrelated random variables. A first technique for generation of correlated random variables has been proposed by [15]. One approach has been to find Latin hypercube samples in which the input variables have small correlations. Authors of [15] perturbed Latin hypercube samples in a way that reduces off diagonal correlation – they diminished an undesired random correlation. The technique is based on iterative updating of sampling matrix, Cholesky decomposition of covariance/correlation of matrix Y has to be applied. In their method, as a measure of the statistical correlation, the Spearman correlation coefficient is used. The estimated correlation matrix A is symmetric, positive definite (unless some rows have an identical ordering). Therefore the Cholesky decomposition of the matrix A may be performed. The technique can be applied iteratively and it can result in a very low correlation coefficient if generating uncorrelated random variables.
3.2 LHS: Sampling and statistical correlation In the context of numerical simulation methods for structural reliability theory, LHS is based on Monte Carlo type of simulations of vector Y under prescribed probability distributions. Realizations are simulated in a special way: the range of probability distribution function fi(Yi) of each random variable Yi is divided into Nsim equidistant (equiprobable) intervals, where Nsim is the number of simulations planned (number of samples for each random variable). The identical probability 1/Nsim for layers on distribution function is usually used. The representants of the equiprobable intervals are selected randomly; realizations are then obtained 9
by inverse transformation of distribution function. The selection of midpoints as representants of each layer is the most often used strategy: yi,j = Fi−1 (vi,j ) = Fi−1 ((j − 0.5) /Nsim) ,
j = 1, . . . , Nsim
(1)
where yi,j is the j-th sample of i-th random variable Yi (i = 1, . . . , Nvar), Fi−1 is the inverse of cumulative distribution function of this random variable. It could be challenged to this simple methodology. One can criticize reduction of samples selection to the midpoints in intervals (we call it interval median). Such objection deals mainly with the tails of PDF, which mostly influences variance, skewness and kurtosis of sample set. This elementary simple approach was already overcome by sampling of mean values related to intervals, e.g. [13]: Z ξi,j xi,j = Nsim x · fi (y) dx (2) ξi,j−1
where fi is the probability density function of variable Xi and the integration limits are: ξi,j = Fi−1 (j/Nsim ). Samples then represent each one-dimensional Fi (Xi ) marginal PDF better in terms of distance of 1 point estimators from the exact statistics. In particular, the mean value is achieved exactly 1 (analytical expression preserves the mean) j Nsim and estimated variance of data is much closer Xi 0 ξi,j−1 ξi,j to the original one. For some PDFs (including Gaussian, Exponential, Laplace, Rayleigh, Logistic, Pareto, or others) the integral (2) can fi(Xi ) be solved analytically. In case of no or diffiXi cult solution of primitive it is necessary to use xi,j an additional effort: numerical solution of the integral. However, such increase of computa- Fig. 2: Samples as the probabilistic means of intervals tional effort is worthwhile indeed. Samples selected by both described ways are almost identical close excluding those in the tails of PDFs [33]. Therefore more difficult method could be used there only considering the fact that tail samples mostly influence estimated variance of sample set. Generally in both cases, regularity of sampling (the range of distribution function is stratified) ensures good sampling and consequently good estimation of statistical parameters of response using small number of simulations. Having the samples of each marginal random variable ready, we may proceed to the second step of LHS: statistical correlation imposition. There are generally two problems related to LHS concerning statistical correlation: First, during sampling an undesired correlation can be introduced between random variables. For example 10
instead a correlation coefficient zero for uncorrelated random variables undesired correlation, e.g. 0.6 can be generated by random. It can happen especially in case of very small number of simulations (tens), where the number of interval combination is rather limited. Second problem we face is: how to introduce prescribed statistical correlation between random variables defined by the target correlation matrix T . Since the currently known techniques for imposition of statistical correlation in to the table of samples of random vector have some severe restrictions, we have developed a new scheme based on simulated annealing optimization algorithm.
3.3 Stochastic optimization method Simulated Annealing
1≤i≤j≤Nvar
Generate Coff compute Eoff
Save the best config. (copy)
Eoff < Ebest ? yes Coff → Cbest , Eoff → Ebest no
no
i=1 j=i+1
automatic stop yes criteria satisfied?
Possible "hill climbing" Draw u from U ∼ [0, 1) Eoff < Eparent ? no Compute ∆E Compute P (∆E) yes (accept) copy
yes
u < P (∆E)? no
Coff → Cparent Eoff → Eparent
wi,j |Ti,j − Ai,j |
(3) or a norm which takes into account deviations of all correlation coefficients can be more suitable (related to the root mean square error): v u NP NV u V −1 P u w (T − Ai,j )2 u i=1 j=i+1 i,j i,j ρrms = u u NP NV V −1 P t wi,j
generate offspring configuration and compute the offspring norm (penalty function)
user terminate?
yes
no yes
i < Ntrials ?
Cooling
no
Fast escape
max
i=1
Temperature loop
ρmax =
t = t0
Trials at a given temperature
The imposition of prescribed correlation matrix into sampling scheme can be understood as a combinatorial optimization problem [46, 48, 33, 34, 41]: The difference between the target T and estimated (actual, generated) A correlation matrices should be as small as possible. A suitable measure of the distance between T and A matrices can be introduced; a possible norm is the maximal difference of correlation coefficients between matrices:
generate the initial configuration randomly compute parent norm (penalty function) set the initial temp. start of a new trial set at a given temperature
Generate Cparent , save it as Cbest compute Eparent , save it as Ebest
decrease t no
t < tmin ?
The end use the best configuration
Cbest (Ebest )
Fig. 3: Flowchart of the Simulated Annealing algo(4)rithm implementation
This norm is normalized with respect to the number of considered correlation 11
coefficients (entries of lower triangle in the correlation matrix). The corresponding weights wi,j are included because in real applications it can be a greater confidence to one correlation coefficient (good data) and a smaller confidence to another one (just estimation). The norm E (either ρmax or ρrms ) has to be minimized, from the point of view of definition of optimization problem, the objective function is and the design variables are related to ordering in sampling scheme It is well known that deterministic optimization techniques and simple stochastic optimization approaches can very often fail to find the global minimum [21]. They are generally strongly dependent on starting point (in our case the initial configuration of sampling scheme). Such techniques fail and finish with some local minimum such that there is no chance to escape from it. In our problem we are definitely facing the problem with multiple local minima. Therefore we need to use the stochastic optimization method which works with nonzero probability of escaping from local minima. The simplest form is the two-membered evolution strategy which works in two steps: mutation and selection. Step 1 (mutation): In the r-th generation a new arrangement of random permutations matrix used in LHS is obtained using random changes of ranks, one change is applied for one random variable. Generation should be performed randomly. The objective function (norm E) can be then calculated using newly obtained correlation matrix (it is called “offspring norm” and the norm E calculated using former arrangement is called “parent norm”). Step 2 (selection): The selection chooses the best norm between the “parent” and “offspring” to survive: For the new generation (permutation table arrangement) the best individual (table arrangement) has to give a value of the objective function (norm E) smaller than before. Such an approach has been intensively tested using numerous examples. It was observed that the method in most cases could not capture the global minimum. It failed in a local minimum and there was no chance to escape from it, as only the improvement of the norm resulted in acceptance of “offspring”. More efficient technique had to be applied. The step “Selection” can be improved by Simulated Annealing approach, a technique which is very robust concerning the starting point (initial arrangement of random permutations table). The Simulated Annealing is optimization algorithm based on randomization techniques and incorporates aspects of iterative improvement algorithms. The difference compared to simple approach described above is that there is a chance to accept offspring leading to a worse norm and such chance is based on the Boltzmann probability distribution using the difference between the norms E before and after random change (parent and offspring norm). There are two possible branches to proceed in the step 2 (selection): 1. New arrangement (offspring) results in decrease of the norm E. Naturally “offspring” is accepted for the new generation. 12
2. New arrangement does not decrease the norm E. Such “offspring” is accepted with the Boltzmann probability depending on te current temperature t [46, 48]. As a result there is much higher probability that the global minimum is found in comparison with deterministic methods and simple evolution strategies. The prescribed combinatorial optimization problem is constrained in the sense that all possible elements of correlation matrix are always within the interval h−1; 1i. Based on this fact the maximum of the norm E can be estimated using prescribed and hypothetically “most remote” matrices T from A, so the initial setting of parameters can be performed without the guess of the user and the “trial and error” procedure. The initial temperature has to be decreased step by step, e.g. using reduction factor after constant number of iterations (e.g. thousands) applied at current temperature ti+1 = ti · 0.95. Note that more sophisticated cooling schedules are known in Simulated Annealing theory [16, 21]. As the number of simulations increases, the estimated correlation matrix is closer to the target one. Figure 18 shows the decrease of norm during SA-process. Such figure is typical and should be monitored. 1
2
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16 32 64 128 256 512 1024 2048
1
2
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16 32 64 128 256 512 1024 2048
1
ρrms
-1
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ρmax
-2
ub) ρr(m s
-3
ρrRmC
10
s
10
ρrRGS
-4
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ρr(m=) s
ms
2.2
-5
10
ρrSA
Nvar
ms
1
256 128 64 32 16 8 4
-6
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-7
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Uncorrelated variables
-8
10 1
ρrms
-1
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ρmax
-2
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-4
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Separable correlation
-5
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Key: max. µ+σ µ µ−σ min.
-6
10
-7
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−1
0
+1
-8
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100 Sample size Nsim
1000
1
10
100 Sample size Nsim
1000
Fig. 4: Results of performance study. Top: Uncorrelated variables; Bottom: Separable correlation. Left: ρrms ; Right: ρmax . Bottom left: Image of correlation matrix (absolute values of Ti,j and distribution of correlation coefficients in T for the separable correlation)
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4
SIMULATION OF RANDOM FIELDS 4.1 Introduction
Stochastic finite element method (SFEM) had facilitated the use of random fields in computational mechanics. Many material and other parameters are uncertain in nature and/or exhibit random spatial variability. Efficient simulation of random fields for problems of stochastic continuum mechanics is in the focus of both researchers and engineers. Achievements in stochastic finite element approaches increased the need for accurate representation and simulation of random fields to model spatially distributed uncertain parameters. The spatial variability of mechanical and geometrical properties of a system and intensity of load can be conveniently represented by means of random fields. Because of the discrete nature of the finite element formulation, the random field must also be discretized into random variables. This process is commonly known as random field discretization. Various methods have been developed for the representation and simulation of random fields utilized within the framework of SFEM [23, 18]. In following we will deal with random fields simulation based on orthogonal transformation of covariance matrix in connection with different types of Monte Carlo simulation. These methods produce stationary and ergodic Gaussian processes. We will focus on error assessment of simulated fields and utilization of LHS methodology thoroughly discussed in the preceding chapter. Since the computational effort in reliability problem is proportional to the number of random variables it is desirable to use small number of random variables to represent a random field. Simulation of the random field by a few random variables is especially suitable for problems where theoretical failure probability should be calculated. To achieve this goal, the transformation of the original random variables into a set of uncorrelated random variables can be performed through an eigenvalue orthogonalization procedure [18]. It is demonstrated that a few of these uncorrelated variables with largest eigenvalues are sufficient for the accurate representation of the random field. The error induced by such truncation will be an object of study in this chapter as well. 4.2 Orthogonal transformation of covariance matrix Suppose that a spatial variability of random parameter is described by the Gaussian random field H(x), x = (x, y, z) is the vector coordinate which determines the position on the structure. A continuous field H(x) is described by discrete values H(xi ) = H(xi, yi, zi), where i = 1, . . . , N denotes the discretization point. As the randomness of the spatial variability in 3-dimensional nature is generally not isotropic, the autocorrelation function of the spatial homogeneous random field is supposed to be a function of the distances between two points |∆x|, |∆y| 14
and |∆z|. The following commonly used exponential form of an autocorrelation function is considered: pow pow pow |∆y| |∆z| |∆x| Raa (∆x, ∆y, ∆z) = exp − − − (5) dx dy dz in which dx , dy and dz are positive parameters called correlation lengths. With increasing d a stronger statistical correlation of a parameter in space is imposed and opposite. The random variables needed for discrete representation of random field in discretization points can be transformed to the uncorrelated normal form by solution of an eigenvalue problem [18]. In order to reduce the computational effort, an eigenvalue orthogonalization procedure can be employed: CXX = ΦΛΦT , where CXX is the covariance matrix. The matrix Φ represents the orthogonal transformation matrix (eigenvectors). The covariance matrix in the uncorrelated space Y is the diagonal matrix Λ = C YY , where the elements of diagonal are the eigenvalues (λ1 , λ2, . . . , λN ) of covariance matrix C XX . Usually, not all eigenvalues have to be calculated and considered for next step (simulation) as the fluctuations can be described almost completely by a few random variables. This can be done by arranging the eigenvalues in descending order, calculating the sum of the eigenvalues up to the i-th eigenvalue and dividing it by trace of Λ. The reduction of number of random variables in fact depends on relationships between total dimensions and discretization of the structure (model) and given correlation lengths. Let the chosen number of important dominating random variables by eigenvalue analysis be Nvar . Now, the eigenvector matrix Φ denotes the reduced eigenvector matrix containing only the respective eigenvectors to the Nvar most important eigenvalues. Then the vector of uncorrelated Gaussian random variables Y T = [Y1, Y2, . . ., YNvar ] can be simulated by a traditional way (Monte Carlo simulation). p variables of vector Y have mean zero and standard deviation √ √The random λ1 , λ2 , . . . , λNvar . The transformation back into correlated space yields the random vector X (random field) by the relation: X = ΦY
(6)
4.3 Novel technique for simulation of cross correlated non-Gaussian random fields A practical framework for generating cross correlated fields with a specified marginal distribution function, an autocorrelation function and cross correlation coefficients is presented in the chapter [35]. The approach relies on well known series expansion methods for simulation of a Gaussian random field. The proposed method requires all cross correlated fields over the domain to share an identical au15
tocorrelation function and the cross correlation structure between each pair of simulated fields to be simply defined by a cross correlation coefficient. Such relations result in specific properties of eigenvectors of covariance matrices of discretized field over the domain. These properties are used to decompose the eigenproblem which must normally be solved in computing the series expansion into two smaller eigenproblems. Such a decomposition represents a significant reduction of computational effort. Non-Gaussian components of a multivariate random field are proposed to be simulated via memoryless transformation of underlying Gaussian random fields for which the Nataf model is employed to modify the correlation structure. In this method, the autocorrelation structure of each field is fulfilled exactly while the cross correlation is only approximated. The associated errors can be computed before performing simulations and it is shown that the errors happen especially in the cross correlation between distant points and that they are negligibly small in practical situations. The technique extends the current methods for simulation of univariate random fields by exploiting special properties of eigenspectra of related covariance matrices of cross correlated random fields. Fig. 5 gives an overview of the current techniques and compares them to the proposed methodology. Fig. 6 illustrates the meaning of single cross correlation coefficients using simulation of three-variate vector random field simulated via the proposed technique over a rectangular domain. xr = x 1
a)
x
2
x
... xN.N F
3
b)
xr = x 1
x
c)
...
2
F
1
F
Φ ,Λ
NF,r
xr = x 1
x
D ΦD I , ΛI
c D= c D c D c D
x
x
2
3
2
...
x
NF,r
d)
D ΦD I , ΛI
...
cD
NF
c D= c D c D c D 1
2
3
...
cD
NF
Φu N
ψ[Φu ]
ψ[Φu ]
Φu 1
Φu 2
Φu 3
Λu
Λu
Λu 1
Λu 2
Λu 3
...
Λu N
H1
H2
H3
...
HNF
H
H1
H2
H3
...
HNF
H1
H2
H3
...
HNF
F
F
Fig. 5: a) Simulation of a univariate random field using Nvar eigenmodes; b) illustration of the method due to Yamazaki and Shinozuka (1990); c) proposed method for simulation of cross correlated fields in two steps when components share the same distribution; d) proposed method for components with different distributions, where eigenanalysis of each field is performed separately.
Latin Hypercube Sampling utilization An increased efficiency of the approach using a reduced set of dominant variables can be gained by usage of variance-reduction techniques (such as LHS) for simulation of uncorrelated random variables [20, 47, 49]. We will show some fur16
a) Random field H1 ( f t )
3 2 1 0 -1 -2
b) Random field H2 ( E )
3 2 1 0 -1 -2
k,k
F1,2 k,l
F1,1 10
xk 20
20 15 10 C 1,2 = 0.8 0
xl 30
40 50 0 c) Random field H3 ( GF )
5
C 1,3 = 0.2
0
k,l
20
F1,2
0.
,3
C2
3 2 1 0 -1 -2
xl 30
5
10
15
40 50 0 d) Profile of estimated statistics (field 1) std. deviation
5
=
10
xk 20
1
mean value 0 20
0
10
20
30
40
50 0
5
10
20
15 0
10
20 x
30
40
50 0
5
15 10 y
Fig. 6: a), b) and c) Random realization of simulated three-variate Gaussian random field, illusi,j tration of the meaning of correlation coefficients Fk,l . d) Profile of the estimated mean value and standard deviation of ft -field (Nsim = 1000).
ther improvements and detailed error assessment of such combined approach. A comparison with classical Monte Carlo simulation (MCS) reveals the superior efficiency and accuracy of the method. The key point is that matrix Y of random variables from the uncorrelated space is assembled with utilization of stratified sampling LHS. It is expected that the superiority of this stratified technique comparing MCS will continue also for accurate representation of random field, thus leading to a decrease of number of simulations needed. This should be proved at least numerically. The methodology for an assessment of error of simulations is described in the next section. 4.4 Error assessment of random field simulation When any method for random field simulation is used it is required that the statistical characteristics of the field generated be as close as possible to the target parameters. Generally, the mean values, variances, correlation and spectral characteristics (statistics) cannot be generated with absolute accuracy. Basic information about random field is captured by its second moment characteristics, i.e. the mean function and the covariance function. Some samples of random fields for a parameter are simulated from the population parameters. A certain statistic of the particular 17
simulation may be very close to or quite far away from the value of corresponding target parameter. When the seed of the pseudo-random number generator is changed other random fields are generated and other values of all sample statistics are naturally obtained. Therefore, each of these statistics can be considered as a random variable with some mean value and variance. The simulation technique is considered as best one which gives an estimated mean value of the statistics very close to the target mean value and also closest to zero variance of the statistics. In our case of zero mean value and unit variance of random field (basic target statistical parameters) we expect to get estimated mean around zero and variance around one. Reduction of spurious correlation What are the consequences of spurious correlation to autocorrelation function variability of simulated random fields? The study has been done for correlation length 1 m and for two numbers of simulations - an error assessment based on samples simulations from population is described later. The results are shown in Fig. 7, mean values and the scatterband represented by mean ± standard deviation of autocorrelation function is plotted. Figure 7a) shows the result for 32 simulations, spurious correlation is not diminished (LHS-mean-SC). It is obvious that capturing of target autocorrelation function is weak and the scatterband is large. The explanation is clear, using only 32 simulations leads to large both norms ρmax and ρrms . When Nsim increases to 64, capturing of autocorrelation function is better, Fig. 7c), d). Note that now the alternative with diminished spurious correlation by SA resulted in excellent function capturing with very small variability, see figure 7d). This fact corresponds with both norms which are in case d) very small. It can be seen that the spurious correlation at the level of simulation of independent random variables influences negatively the autocorrelation function. These illustrative indicate that norms used as objective functions in Simulated Annealing algorithm can be interpreted as a qualitative prediction of resulting quality of autocorrelation structure. Classification of sampling schemes When any method for random field simulation is used, it is required that the statistical characteristics of the field generated should be as close as possible to the target statistical parameters. Generally, the mean values, standard deviations, correlation and spectral characteristics (we will use the common term “statistics”) cannot be generated with absolute accuracy. All possibilities of sampling method can be summarized as follows: (i) crude Monte Carlo simulation (MCS); (ii) Latin hypercube sampling under original scheme, [19] , (LHS-median); (iii) Latin hypercube sampling under improved scheme, [13] (LHS-mean). These schemes can be applied in two alternatives: (1) No attention is paid to spurious correlation (SC); (2) Spurious correlation diminished (SCD) There are 6 combinations, cases with SC and SCD, which are sampled by MC, LHS-half and LHS-mean. What is the best alternative? Naturally, the quality of sampling schemes can be intuitively predicted 18
CHH (D x)
1 0.8 0.6 0.4 0.2 0
a)
N Sim = 32, RSC
b)
N Sim = 32, SCD
eS = 0.56
1 0.8 0.6 0.4 0.2 0 -0.2
c)
eS = 0.37
N Sim = 64, RSC
d)
N Sim = 64, SCD
eS = 0.43
0
2
4
6
8
eS = 0.01
0 2 Space lag D x
4
6
8
10
Fig. 7: Scatterband of autocorrelation function Caa (ξ) for Nsim = 32: a) LHS-mean-SC; b) LHSmean-SCD; and Nsim = 64: c) LHS-mean-SC; d) LHS-mean-SCD
even without numerical experiment, e.g. combination (MCS) and (SC) should definitely belong to worst case and combination of (LHS-mean) and (SCD) should be the most efficient. The assessment can be done by performing more runs of the same simulation process with a different random setting of the seed of pseudo random number generator. Thus samples are artificially generated from the population in this way. Let us consider 1D structure of length 10 m (e.g. beam), the structure is divided into 128 discretization points associated with finite elements (N = 128). The region of small number of simulations (Nsim = 8, 16, 32, 64, 128, 256, 512) has been selected in parametric study - implicitly it was supposed that the superiority of LHS should appear for small number simulations (tens, hundreds). Number of runs Nrun = 30 was selected for estimation of statistics. So the random fields had to be simulated Nrun × Nsim times for a statistics of interest. The results are plotted in Fig. 8. Mean value: An ability to simulate mean value of random field is excellent in all alternatives of LHS (figures a) and b)), even for very low number of simulations. This ability is rather poor in case of MCS, mean value of mean fluctuates and standard deviation of mean is high in comparison to LHS. Standard deviation: The ability to simulate standard deviation of random field is documented in figures c) and d). Again, capturing of this statistics is “random” in case of MCS, standard deviation of standard deviation is high in comparison to LHS. LHS-half underestimates mean value of standard deviation (figure c)) for low number of simulations. The capability of improved sampling scheme LHS-mean is much better and convergence to target statistic (unit standard deviation) is faster. This is a general feature of LHS tested at the level of random variables. An important fact is documented: dimin19
ishing spurious correlation has small influence on these basic statistics of random field. Note, that if we construct statistics presented in Fig. 8 for different correlation length of the field, similar trends will be obtained. 0.03
0.16
0.02
0.14
0.01
0.12
LHS-mean- SCD SC LHS-half- SCD SC
0.1
0
0.08
-0.01
MCS- SCD SC
0.06
-0.02
a)
-0.03 -0.04
0.04
b)
0.02 0
8
16
32
64
1.02 1.01 1 0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.92
128 256 512
c) 8
16
32
64
8 0.1 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0
16
32
64
128 256 512
LHS-mean- SCD SC LHS-half- SCD SC MCS- SCD SC
d)
128 256 512 8 16 Number of simulations n
32
64
128 256 512
Fig. 8: Statistics of mean and standard deviation (d = 1 m): a) mean of mean; b) standard deviation of mean; c) mean of standard deviation; d) standard deviation of standard deviation
20
5
MULTI-FILAMENT YARNS AND TRC
The tensile failure of fiber-reinforced composites (such as the textile reinforced concrete-TRC) is generally dominated by failure of the fiber bundle. The matrix material, whether polymer, ceramic or metal, serves mainly to transfer the load among the fibers through the elasticity, or yielding, debonding with sliding friction between the fiber and matrix. The matrix can carry some load in a metal or polymer matrix composite but, after matrix cracking, carries almost zero load in ceramic matrix composites. The two factors controlling fiber failure are (i) the statistical fiber strength and (ii) the stress distribution along the fiber direction. The stress along a fiber depends on the applied stress, but also on precisely how stress is transferred from a broken fiber to the surrounding intact fibers and matrix environment. This stress transfer is governed by the elastic properties of the constituents and by the fiber/matrix interface, and is difficult to obtain in the presence of more than one broken fiber. There are two load sharing rules. One is “global load sharing”, i.e. loads dropped due to fiber break(s) are shared equally among intact fibers. The other is “local load sharing”, i.e. dropped loads are carried preferably by filaments surrounding the broken filaments.
Fig. 9: Yarn specimen with resin clamps.
The presented work [43, 29, 30, 36, 28] is focused particularly on fiber bundles under global (equal) load sharing. In particular we are focusing on size (length) effect of multi-filament yarn under tensile strength test. The available statistical models of strength of bundles are reviewed. Deterministic micromechanical computational model capable of tracing the whole load-deflection curve has been developed and used for identification and study of sources of randomness affecting the evolution of the stiffness during the loading of yarns in tension. It has been shown that also the stiffness evolution in the early stages of loading influences the maximum tensile force in the bundle. The model serves as a basis for a complex stochastic analysis of the complex size effects including all mentioned effects employing the random field simulation technique. Such stochastic modeling framework has been used for derivation of new size effect laws for each of the considered sources of randomness separately. In order to introduce the statistical length scale in the Weibull power law for the mean size effect, we modify the classical law by introducing the length-dependent 21
function f (l) with the filament length l in the following form σ (l) = s0 [− ln (1 − Pf )]1/m f (l)
(7)
where m is the Weibull modulus (shape parameter of Weibull strength distribution of fiber strength), s0 is the scale parameter of strength distribution and Pf is the failure probability. The author suggests to approximate the numerically obtained size effect on fibers with local tensile strength f t described by random field by Eq. (7) with f (l) expressed by one of the following formulas: −1/m 1/m l lρ lρ + or f (l) = (8) f (l) = lρ lρ + l lρ + l This introduces the statistical length scale into (local) Weibull law in form of autocorrelation length lρ of random strength field. Since the description of strength by random field still does not suffice to capture the size effects measured by real experiments, we have also studied and developed algorithm for back identification of delayed activation ε0 of fibers. Shortly, if fibers between clamps do not have equal length, the longer ones activate in later stages of the test. If we identify also parameters of stiffness random field (Young’s modulus of elasticity of elastic-brittle fibers E) we are able to simulate all essential features of real tensile test, see Fig. 10. 1000
Yarn force T [N]
800
0.03 m
0.22 m
0.045 m n µθ (e) Ttest (e)
0.5 m
600 400 200 0 800 600 400 200 0 0
1
2
3
4 0 1 Yarn strain e [%]
2
3
4
700 600 500 400 300 200 100 0 600 500 400 300 200 100 0
0
1
2
0.03 m
0.22 m
0.045 m n µ0 (e) Ttest (e)
0.5 m
3
4 0 1 Yarn strain e [%]
2
3
4
Fig. 10: Comparison of numerical simulations (gray) with experiments (black). Left: Simulations without delayed activation, randomized stiffness and strength. Diagram computed with DA plotted with dashed line. Right: Simulations with included delayed activation, randomized stiffness and strength. Diagrams computed with mean values plotted with dashed line.
The resulting size effect curve (double plot of length against strength of yarn) is plotted in Fig. 11. In addition to the size effect curves obtained from the random process simulations Fig. 11 also shows the size effect obtained with the Daniels’s [9] and Smith’s [22] models calculated with nf = 1600. Assuming that the filaments follow the Weibull 22
1000
1.0
Ideal yarn
Peak yarn force T [N]
900 800
10
Ideal yarn with slack (θ)
* (Daniels, Smith) m*σ @mσ,n Eqn. (27)
Asymptotes for ll → →0∞
700
0.639 0.612
600 500
400 0.001
Simulations with random: σ σ, E σ, E and θ
m 1 Experiment
Yarn mean strength efficiency [-]
1100
Yarn length in number of correlation lengths [-] 0.01 0.1 1
0.377 0.01
0.1
l r= 0.3
1
3
Yarn length l [m]
Fig. 11: Size effect curves obtained numerically for the randomized f t , ε0 , ft together with E and all three parameters simultaneously.
scaling we may construct the bundle power law as a product of Daniels’s prediction of the mean total strength [9] with the Weibull scaling f (l) = (l0/l)1/m 1/m 1/m l l0 0 µ⋆ (l) = µ⋆ f (l) = µ⋆ = s0 · m−1/m · e−1/m . l l
(9)
Based on the lessons learned from the numerical analysis the author has suggested approximation formulas describing the size effect laws due to the random strength or stiffness along the bundle. The obtained results have been verified with the help of the available analytical and numerical fiber bundle models by Smith and Daniels. However, the available fiber bundle models could not be used for modeling the response measured in the yarn tensile test, because they impose practically unachieveable assumptions of regular force transmission in the clamping and do not capture the disorder in the structure of filaments in the bundle.
6 INTERACTION OF ENERGETIC AND STATISTICAL SIZE EFFECTS IN QUASIBRITTLE FAILURE Most materials - composites, granular media, metals, biomaterials, etc. - possess microstructures displaying several length scales, oftentimes accompanied by a non-deterministic disorder. A better understanding and prediction of the resulting random nature of materials’ micro- meso- and/or macro-scopic properties requires a modeling approach based on a combination of probabilistic concepts with meth23
ods of mechanics. By large, one has to resort to computational methods given the insurmountable computational difficulties. This presents some of current advances in that field with focus on heterogeneous concrete structures and scaling laws of concrete fracture. 6.1 Case study on dog-bone specimens F F
weakened layer thickness
0.6 D
monitored vertical displacements uupp and ulow
stiff steel platen
F D D/4
1.6
deterministic computation (microplane model)
large size asymptote
1.0 0.9 0.8
A
B
C
D
E
F
1.4 10
1.4 1.3 1.2 1.1
100
1000
experiments (mean ± std. dev.) 10000
specimen size D [mm] (characteristic dimension)
0.7
0.6 100000
D
r
normalized nominal strength [-]
1.8
small size strength asymptote
8 mm
2.0
D/5 1.6
0.5 mm
3.4 3.2 3.0 2.8 2.6 2.4 2.2
weak layer thickness: 2 mm
nominal strength s N [MPa]
stiff steel platen
D/4
D
r
mm
mm
A 50 36.25 B 100 72.5 C 200 145 D 400 290 E 800 580 F 1600 1160
100 mm Nom. strength: Spcs Aver. (std.dev.) # MPa 2.54 (0.41) 2.97 (0.19) 2.75 (0.21) 2.30 (0.09) 2.07 (0.12) 1.86 (0.16)
10 4 7 5 4 4
Fig. 12: Top: Dog-bone specimens tested by van Vliet and van Mier (1998): series A to F, 2D modeled in ATENA software together with a surface weak layer (top left); Left bottom: size effect plot for experimental compared to ‘deterministic’ and ‘weak layer’ computations. Top right: computed field of principal tension over the specimen in an elastic stress state.
In this chapter, we attempt the identification, study and modeling of possible sources of size effects in concrete structures acting both separately and together [32, 31, 45]. A particular motivation is in the interplay of several identified scaling lengths stemming from the material, boundary conditions and geometry. Methods of stochastic nonlinear fracture mechanics are used to model the well published results of direct tensile tests of dog-bone specimens with rotating boundary conditions. Firstly, the specimens are modeled using microplane material law to show that a large portion of the dependence of nominal strength on structural size can be explained deterministically with the help of deterministic length represented by crack band width in our model. Secondly, further strength dependence on size in large specimens is modeled by an autocorrelated random strength field. The important statistical length scale is introduced in the form of the autocorrelation length 24
PDF(K1)
2
K1 (.10-5)
1
2.4 0
2.0 0
0.5
1
1.5
2
2.5
Autocorrelation R
d
0 0.5 1 1.5 2 2.5 3 l r 1 0.8 0.6 0.4 0.2 0
lr 0
50
100 150 200
Space lag d [mm]
1.6 1.2 0.8
0 100 200 300 400 500 600 700 x [mm]
800 0
1200 1000 800 600 400 200 y [mm]
Fig. 13: Top-left: Weibull probability distribution function of the randomized parameter K1. Bottomleft: Autocorrelation function. Right: Realization of a Weibull random field of K1 compared with dog-bone specimens type A – E. The dashed lines correspond to the mean and mean ± one standard deviation of K1
of the field. It is shown that the inhomogeneity of material properties over the structure in the form of an autocorrelated random strength field gives rise to imperfections that trigger fracturing in highly stressed regions of a structure. In addition, the strength drop noticeable with small specimens which was obtained in the experiments is explained by the presence of a weak surface layer of constant thickness (caused e.g. by drying, surface damage, aggregate size limitation at the boundary, or other irregularities). All three named sources ( deterministic-energetic, statistical size effects, and the weak layer effect) are believed to be the sources most contributing to the observed strength size effect; the model combining all of them is capable of reproducing the measured data. The asymptotic size effect form caused by random strength is the classical Weibull power law. By random sampling of the local strength field we were also able to model the random scatter of resulting nominal strengths. The last effect presented here is the weak boundary layer of constant width. This weakened layer results in a reduction in the strength of small specimens which contrasts with the trends of the two previous size effects. The asymptotic properties of all sources and their combinations are given. Also, simple scaling rules, anchored in theoretical dimensional analysis, are suggested. In such a model a complex interplay of three scaling lengths is captured at a time. The computational approach represents a marriage of advanced computational nonlinear fracture mechanics with simulation techniques for random fields representing spatially varying material properties. Using a numerical example, we document how different sources of size effects detrimental to strength can interact and result in relatively complex quasibrittle failure processes. Numerical simulations of localization phenomena demonstrate that the introduction of the stochastic distribution of material properties reveal phenomena that would otherwise remain un25
A 22
B 14
C 22
C 34
C 51
C 55
l r = 80 mm cb = 8 mm D3
A D 22
D 27
c b = 8 mm
D 44
D 47
l r = 80 mm
B
E2
E3
E 10
E 15
E 18
F 13
F 26
F 42
E 27
lr = 80 mm
C F3
D 55
F 52 low strength
D
lr = 80 mm
high strength
Fig. 14: Simulated random strength field realizations and corresponding crack patterns in deformed specimens right after attaining the maximum force Fmax . Fields were simulated and crack widths were computed at the integration points of finite elements
noticed. The presented study documents the well known fact that the experimental determination of material parameters (needed for the rational and safe design of structures) is very difficult for quasibrittle materials such as concrete. 26
6.2 Introduction and development of the new size effect law The size effect on nominal strength σN of concrete structures has basically two explanations, deterministic (energetic) and statistical (probabilistic). The former is caused by the stress redistribution on the fracture process zone, which is for different structure sizes about the same. The latter is explained by higher probability of low local strength for large structures. Practical and simple approach to incorporate the statistical size effect into the design or the assessment of very large unreinforced concrete structures (such as arch dams, foundations and earth retaining structures, where the statistical size effect plays a significant role) is important. Failure load prediction can be done without simulation of Monte Carlo type utilizing the energetic-statistical size effect formula in mean sense together with deterministic results of FEM nonlinear fracture mechanics codes. We propose a new improved law with two scaling lengths (deterministic and statistical) for combined energetic-probabilistic size effect on the nominal strength for structures failing by crack initiation from smooth surface. The role of these two lengths in the transition from energetic to statistical size effect of Weibull type is clarified. Relations to the recently developed deterministic-energetic and energeticstatistical formulas are presented. We also clarify the role and interplay of two material lengths: deterministic and statistical. The deterministic energetic size effect formula for crack initiation from smooth surface reads [2, 6, 3]: 1/r rD b (10) σN (D) = fr∞ 1 + D + lp where σN is the nominal strength depending on the structural size D. Parameters fr , Db and r are positive constants representing the unknown empirical parameters to be determined. Parameter fr represents solution of the elastic-brittle strength which is reached as a nominal strength for very large structural sizes. The exponent r (a constant) controls the curvature and the slope of the law. The exponent offers a degree of freedom while having no effect on the expansion in derivation of the law [2, 6]. Parameter Db has the meaning of the thickness of cracked layer. Variation of the parameter Db moves the whole curve left or right; it represents the deterministic scaling parameter and is in principle related to grain size and drives the transition from elastic brittle (Db = 0) to quasibrittle (Db > 0) behavior. By considering the fact that extremely small structures (smaller than Db ) must exhibit the plastic limit, a parameter lp is introduced to control this convergence. The formula (1) represents the full size range transition from perfectly plastic behavior (when D → 0; D ≪ lp) to elastic brittle behavior (D → ∞; D ≫ Db ) through quasibrittle behavior. Parameter lp governs the transition to plasticity for small sizes D (crack band models or averaging in nonlocal models leads to horizon27
tal asymptote). The case of lp 6= 0 shows the plastic limit for vanishing size D and the cohesive crack and perfectly plastic material in the crack both predicts equivalent plastic behavior. For large sizes the influence of lp decays fast and therefore the cases of lp 6= 0 are asymptotically equivalent to case of lp = 0 for large D. The large-size asymptote of the deterministic energetic size effect formula (10) is horizontal: σN (D)/fr = 1, see Fig. 16a). But this is not in agreement with the results of nonlocal Weibull theory as applied to modulus of rupture [4], in which the large-size asymptote in the logarithmic plot has the slope −n/m corresponding to the power law of the classical Weibull statistical theory [25]. In view of this theoretical evidence, there is a need to superimpose the energetic and statistical theories. Such superimposition is important, for example, for analyzing the size effect in vertical bending fracture of arch dams, foundation plinths or retaining walls. The statistical part of size effect and the existence of statistical length scale have been investigated in detail by the previous chapter for the particular case of glass fibers. By incorporating the result into the formula (10) we get a final law: " #1/r r n/m L0 rDb σN = fr∞ + (11) L0 + D lp + D This formula exhibits the following features: • Small size left asymptote is correct (deterministic), parameter lp drives to fully plastic transition for small sizes. • Large size asymptote is the Weibull power law (statistical size effect, a straight line with the slope -n/m in the double-logarithmic plot of size versus nominal strength) • The formula introduces two scaling lengths: deterministic (Db ) and statistical (L0). The mean size effect is partitioned into deterministic and statistical parts. Each have its own length scale, the interplay of both embodies behavior expected and justified by previous research. Parameter Db drives the transition from elastic-brittle to quasibrittle and L0 drives the transitional zone from constant property to local Weibull via strength random field. Note that the autocorrelation length lρ has direct connection to our statistical length L0. This correspondence is explained in the author’s dissertation [38]. Having the summation in the denominators limit both the statistical and deterministic parts from growing to infinity for small D. So it remedies the problem that the previous energetic-statistical formulas [4] intersect the deterministic law at the size D = Db and therefore gives higher mean nominal strength prediction for small structures compared to the deterministic case. Note that for m → ∞ it degenerates to deterministic formula (10). The same applies if L0 → ∞. The interplay of two scaling lengths using the ratio L0/Db is demonstrated in [42, 26]. The question arises what is in reality the ratio L0/Db ? Since both scaling lengths are in concrete 28
probably driven mainly by grain sizes, we expect L0 ≈ Db , so the simpler law with L0 = Db should be an excellent performer for practical cases.
Water flow Displacement of abutment
Fig. 15: Malpasset dam ( Left: Photos of Malpasset Dam before and after failure (taken from http://www.aude.pref.gouv.fr/ddrm/risque-barr/bar2.html). Right: Sketch of failure mechanism adapted from [17] (the cracks, reproduced from this source, and now found to be realistic only for a 1/10-scale reduced scale model; in reality they must have localized into one main crack
6.3 Superimposition of deterministic and statistical size effects As was already mentioned deterministic modeling with NLFEM can capture only deterministic size effect. A procedure of superimposition with statistical part should be established. Such procedure of the improvement of the failure load (nominal stress at failure, deterministic size effect prediction) obtained by a nonlinear fracture mechanics computer code can be as follows: 1. Suppose that the modeled structure has characteristic dimension Dt . The natural first step is to create FEM computational model for this real size. At this level the computational model should be tuned and calibrated as much as possible (meshing, boundary conditions, material etc.). Note that we obtain a prediction of nominal strength of the structure (using failure load corresponding to the peak load of load-deflection diagram) for size Dt , but it reflects only deterministic-energetic features of fracture. Simply, the strength is usually overestimated at this (first) step, the overestimation is more significant as real structure is larger. Result of this step is a point in the size effect plot presented by a filled circle in Fig. 16 a). 2. Scale down and up geometry of our computational model in order to obtain the set of similar structures with characteristic sizes Di , i = 1, . . . , N . Based on 29
numerical experience a reasonable number is around 10 sizes and depends how the sizes cover transition phases. Therefore, sizes Di should span over large region from very small to very large sizes. Then calculate nominal strength for each size σN,i , i = 1, . . . , N . Note that for two very large sizes nominal strengths should be almost identical as this calculation follows energetic size effect with horizontal asymptote. If not, failure mechanism is not just only crack initiation, other phenomena (stress redistribution) plays more significant role and the procedure suggested here cannot be applied. The computational model has to be mesh-objective in order to obtain objective results (eg. crack band model, nonlocal damage continuum) for all sizes. In order to ensure that phenomenon of stress redistribution (causing the size effect for the range of sizes) is correctly captured, well tested models are recommended for strength prediction. A special attention should be paid to the selection of constitutive law and localization limiter. The result of this step is a set of point (circles) in the size effect plot as shown in Fig. 16 a). 3. The next step is to obtain the optimum fit of the deterministic-energetic formula (10) using the set of N pairs ({Di, σN,i} : i = 1, . . . , t, . . . , N ). Since the deterministic formula is generally nonlinear in fitted parameters (if r 6= 1 or lp 6= 0) the algorithm for nonlinear regression fit is needed.
The parameter lp can be excluded from the fit based on the plastic analysis. Fit of the parameter fr can also be avoided because this limit can be estimated from nonlinear FEM analysis as the value to which the nominal strength converges with increasing size. So we can be prescribe (for very large sizes), σN /fr = 1 as asymptotic limit. The result of this step is illustrated by a fitted curve to the set of points in figure 16a).
4. There are three remaining parameters which should be substituted into statistical-energetic formula (11): n, m and L0 : Parameter n is the number of spatial dimensions (n = 1, 2 or 3). Parameter m represents the Weibull modulus of FPZ with Weibull distribution of random strength. Recent study [3] reveals that, for concrete and mortar, the asymptotic value of Weibull modulus m ≈ 24 rather than 12, the value widely accepted so far. Ratio n/m therefore represents the slope of MSEC in size effect plot for D → ∞. This means that for extreme sizes the nominal strength decreases, for two-dimensional (2D) similarity (n = 2), as the −1/12 power of the structure size. Note, that for different material the asymptotic value of Weibull modulus is different, eg. for laminates much higher than 24. Result of these 4 steps are shown for illustration in Fig. 16a). Parameter L0 is now only remaining parameter to be determined. As it represents statistical length scale it seems to be that we will need to utilize a statistical software incorporated into your NLFEM code. But there is much simpler alternative based on simple calculation of local Weibull 30
integral. Once the mean strength of a large structure is known (a square in the size effect plot, one can pass a straight line of slope −n/m through the point (Weibull asymptote). Graphically, the intersection of the statistical asymptote with deterministic strength for infinite structure size (horizontal asymptote) fr gives the statistical scaling length on D-axis L0 , see figure 1b).
l p =0 l p ¹0
2
Plastic limit
a)
deterministic NLFEM computation 1
(10)
r
1
r Db 0.6 3
b) mean nominal strength
sN / fr ¥
2
1
Weibull power law n
ls
m
0.6 3
sN / fr ¥
As all parameters of statistical-energetic formula are determined, nominal strength can be calculated for any size. Using real size of the structure Dt the prediction of corresponding nominal strength σN,t can be done using (11). This prediction will be generally different (lower) from initial deterministic prediction, Fig. 16c). The larger the structure the larger difference is. The formula will provide us the strength prediction for the mean strength. Additionally, a scatter of strength can be determined just using the fundamental assumption of Weibull distribution. For the distribution we know two parameters, shape parameter m is prescribed initially, and scale parameters s can be calculated easily from predicted mean and Weibull modulus.
sN / fr ¥
3
l p =0 l p ¹0
2
for Db®0
c)
mean size effect law (11) 1 r
1
Bound on stat. size effect 0.6
Plastic limit
00.1
0.1
n
1
m 10
100
1000
D/Db Fig. 16: Illustration of superimposition steps.
31
7
PROBABILISTIC ADAPTIVE FINITE ELEMENT METHOD
This section is devoted to computational mechanics techniques for structures with (multiple) random properties. Concrete is an important engineering material that is used widely in large structures that are subject to a variety of complex loads such as earthquakes, snow loads and air blasts. Safety analyses require that mathematical models must simulate inelastic behavior that often combines elasticity, plasticity, continuum damage and material failure. In general quasi-brittle materials such as ice and rock also exhibit similar features so contributions for one material are generally applicable to another. Because of this complex behavior almost all investigations must, of necessity, involve numerical procedures and therefore fall under the broad category of computational mechanics. In recent years, the concept of adaptive modeling in engineering simulations has seen significant advances. Adaptive modeling goes beyond the usual steps of mesh refinement and enrichment, and involves “refining” the mathematical model itself. Adaptive modeling methods have been put forward in a number of areas like heterogeneous materials, functionally graded materials, plate- and shell-like structures, fluid mechanics, molecular mechanics and multiscale problems. This chaper is aimed at presenting ideas behind a computational tool oriented toward adaptive nonlinear simulation driven by spatially varying model properties which is currently under development by authors. In particular, we focus on detailed tracing of the evolution of damage or other nonlinear phenomena during loading of structure with varying properties by nonlinear finite element method with mesh refinement/coarsening in highly/low stressed or damaged regions. We have developed [44] the major ingredients of the algorithm and we present the current stage of progress on the computational platform in this chapter. The computations is illustrated on a simple one-dimensional example involving a bar made of plastic material with hardening under uniaxial tension, see fig. 17. The computational platform is still under development. However, based on the current progress stage few conclusions regarding the next directions can be made. Intuitively, we expect that for sufficiently large quasi-brittle or brittle structures the suggested adaptivity control provides a more pragmatic strategy for the computation of the ultimate load of the structure then any error-controlled refinement. Nevertheless, at the scale of the autocorrelation and/or of the characteristic length, it may turn out that the error-controlled adaptivity algorithm becomes increasingly important in order to reflect the stress gradients in the critical regions appropriately. Therefore, both types of refinement control shall be considered in the later stages of the algorithm development. For this purpose, we will combine the available errorcontrolled adaptive modules with both h- and p- adaptivity employing the hierarchic refinement strategy with the suggested refinement strategy controlled by the propagation of the failure process zone. After the thorough study of the computational 32
aspects of the error-based and material-based refinement controls, the theoretical study of the interaction of the two length-scales will be elaborated. The fine resolution of the stress gradient and the strength in the fracture process zone at the level of the autocorrelation structure and/or the characteristic length provides the basis for the thorough study of both the statistical and the energetic size effect and their interaction. In these studies, we shall adapt the available models for concrete, the crack-band model [5] and the microplane model combined with non-local continua. The concept relies heavily on combining the Monte Carlo generation of samples of random properties and adaptive nonlinear simulation with finite element method. The targets are: to capture the complex phenomena associated with heterogeneous material behavior including damage localization, to study the size effect in all its complexity with both the statistical and energetic components, including their interaction, to significantly reduce the computational cost that arises for any fine resolution of the discretization, and to provide a robust numerical platform for future development and validation of alternative analytical statistical methods based on extreme value theory and system interaction. 2
Load steps: 7 6 5 4 3 2 1
Plastic strain
d) 1.5 1 0.5 0
2
longitudinal stress [Pa]
a)
b)
the last yielding point
c) the last yielding point
σy
1.5
8 K
σy
1
5
1
3 2
E 1
0.5
6
7
4
1
the first yielding point
the first yielding point
0 0
0.5
1 strain ε
1.5
20
0.2
0.4
0.6
bar coordinate x
0.8
1 0 0.5 1 1.5 2 2.5 3 bar elongation u [m]
Fig. 17: Illustration of evolution of plasticity, FE mesh a) varied constitutive law of a material point. b) spatial variation of yield stress σy (barrier of linearity) over the length, and evolution of FE meshes corresponding to load steps c) load-elongation diagram of the whole bar, levels of load steps highlighted. d) evolution of plastic strain over the length in five loading steps.
33
8
FREET SOFTWARE
A multi-purpose probabilistic software for statistical, sensitivity and reliability analyzes of engineering problems has been developed [37]. The software is based on efficient reliability techniques described above and the computational core is implemented by the present author in C++ programming language. The GUI (graphical user interface) is being implemented by Dr. Rusina in C++. The software is designed in the form suitable for relatively easy assessment of any user-defined computational problem written in C++, FORTRAN or any other programming languages. The approach is general and can be applied for basic statistical analysis of computationally intensive problems. The basic aim of statistical analysis is to obtain the estimation of the structural response statistics (failure load, deflections, cracks, stresses, etc.). The FREET software integrated with the ATENA software were used to capture both the statistical and deterministic size effect obtained from experiments. Probabilistic treatment of nonlinear fracture mechanics in the sense of extreme value statistics has been recently applied for crack initiation problems which exhibits Weibull-type the statistical size effect [38].
Fig. 18: Imposing of statistical correlation
9
CONCLUSIONS
Simulation of random variables In chapter 3 the new achievement is mainly the new efficient technique of imposing the statistical correlation based on Simulated Annealing. The technique is robust, efficient and very fast and has many advantages in comparison with former techniques. The increased efficiency of small-sample simulation technique LHS 34
can also be achieved by the proper selection of samples representing the layered probability content of random variables. The methods are implemented by author and constitutes the computation core of the multipurpose software package FREET for statistical, sensitivity and reliability analysis of computational problems. A future work is recommended in: (i) Implementation of advanced method for probabilistic analysis, in particular response surface, FORM and Importance Sampling; (ii) Further research in simulation of random vectors with prescribed simultaneous probability density function or just marginals and covariances. Simulation of random fields Chapter 4 confirms the superior efficiency of LHS and correlation control in the context of sample simulation of random fields. An attempt has been done to show better the role of correlation control - diminishing spurious correlation in random field simulation and importance of sampling schemes for simulation of uncorrelated random variables. It has been shown that a spurious correlation influences significantly the scatter of estimated autocorrelation function of simulated random fields. A clear indication of this scatter is the fulfillment of norms used as objective functions in Simulated Annealing algorithm to diminish spurious correlation at the level of underlying random variables. The quality of simulated samples of random fields should be assessed. An error assessment procedure has been proposed and performed for six alternatives of sampling schemes. Diminishing spurious correlation does not influence the capturing of these statistics but does influence significantly a realization of autocorrelation function of a random field. A future work is recommended in: (i) Study, development and implementation of simulation of non-Gaussian stochastic fields; (ii) The newly developed tools of stochastic computational mechanics in the form of stochastic finite element method (SFEM) will now enable complex numerical investigations. We expect both (i) verification of newly achieved theoretical results (e.g. in the form of the proposed size effect law for quasibrittle failure at crack initiation) and (ii) numerical computations of real examples focused on the influence of nonlinearities on failure probability estimations. Size effect of multi-filament yarns The performed stochastic simulations with the available experimental data revealed the existence of statistical length scale that could be captured by introducing an autocorrelation of random material properties. This represents the departure from the classical Weibull-based models that are lacking any kind of length-scale. The introduced model delivers a quasi-ductile response of the bundle from the ensemble of interacting linear-elastic brittle components with irregular properties. In this respect the present approach falls into the category of lattice models used to model quasi-brittle behavior of concrete. It should be noted, that due to the possibility to trace the failure process in a detailed way both in the experiment and in the simulation, the modeling of multi-filament yarns provides a unique opportunity to study the local effects in quasi-brittle materials. The possibility to generalize 35
the results for other quasi-brittle materials is worth further intensive studies; The obtained statistical material characteristics turned out to be of crucial importance for robust modeling of crack bridges occurring in the cementitious textile composites. The ”well designed” microstructure of the yarn and of the bond layer in the crack bridge may significantly increase the overall deformation capacity (ductility) of structural elements. The lessons learned from the present study will be applied in a more targeted development of new yarn and textile structures with an improved performance of crack bridges. Development of micromechanical model of bond behavior and its coupling with the developed models will be pursued next. Energetic-statistical size effect We have presented a broader theoretical treatment of connections between fiber bundle models and size effect of concrete structures. It has been shown how the statistical size effect at fracture initiation can be captured by a stochastic finite element code based on extreme value statistics, simulation of the random field of material properties and chain of bundles transition. The computer simulations of the statistical size effect in 1D based on stability postulate of extreme value distributions match the test data. However, in some cases the correct behavior cannot be achieved for other tests using a 1D treatment. A proper way of treating the stress redistribution is by the proposed macro-elements in 2D (or 3D), the scaling of which is based on the fiber bundle model capturing partial load-sharing and ductility in the finite element system. A simple and effective strategy for capturing the statistical size effect using stochastic finite element methods is developed which overcomes the problematic feature of stochastic finite element method: How to capture the statistical size effect for structures of very large sizes. The idea is to emulate the recursive stability property from which the Weibull extreme value distribution is derived. Usage of combination of a feasible type of Monte Carlo simulation and computational modeling of nonlinear fracture mechanics renders a probabilistic treatment of complex fracture mechanics problems possible. The approach may be understood as a computational trick based on extreme value theory similar to its counterpart in deterministic nonlinear analysis of fracture - crack band model. The interplay of deterministic and statistical lengths of quasibrittle structures has been clarified and the analytical formula for the nominal mean strength prediction of crack initiation problems has been derived and proposed. The law features two separate scaling lengths of structures governing two different sources of size effect: deterministic and statistical. The role of these two lengths in the transition from energetic to statistical size effect of Weibull type is explained. A practical procedure of superimposition of the deterministic and statistical size effects at crack initiation has been suggested. It requires only a few NLFEM analysis using scaled sizes so the necessity of time consuming statistical simulation is avoided. The prediction can be done without any special Monte Carlo simulation, which is usually used to deal with the influence of uncertainties on structural strength. 36
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ˇ ´ , M., AND N OV AK ´ , D. Correlation control in small sample Monte Carlo [33] VO RECHOVSK Y type simulation. Part I: Optimization technique simulated annealing. Probabilistic Engineering Mechanics (2007), in review. ˇ ´ , M. Correlation control in small sample Monte Carlo type simulation. Part [34] VO RECHOVSK Y II: Performance in and relevance to multivariate modeling. Probabilistic Engineering Mechanics, (2007), in review. ˇ ´ , M. Simulation of simply cross correlated random fields by series expansion [35] VO RECHOVSK Y methods. Structural safety (2007), accepted, in print. ˇ ´ , M., C HUDOBA , R., H ANISCH , V., AND G RIES , T. Effect of twist, fine[36] VO RECHOVSK Y ness, loading rate and length on tensile behavior of multifilament yarns (a multivariate study). Textile Research Journal (2007), accepted, in print. ´ , D., VO RECHOVSK ˇ ´ , M. AND RUSINA , R. FReET. FREET - Feasible Re[37] N OV AK Y liability Engineering Efficient Tool, User’s and Theory guides. Brno/Cervenka Consulting, http://www.freet.cz, Czech Republic, 2006. ˇ ´ , M. Stochastic fracture mechanics and size effect. PhD thesis, Brno Uni[38] VO RECHOVSK Y versity of Technology, Brno, Czech Republic, 2004. ˇ ´ , M. Stochastic computational mechanics of quasibrittle structures. Habili[39] VO RECHOVSK Y tation thesis presented at Brno University of Technology, Brno, Czech Republic, 2007. ˇ ´ , M., N OV AK ´ , D., AND P UKL , R. Statis[40] BA Zˇ ANT, Z. P., PANG , S. D., VO RECHOVSK Y tical size effect in quasibrittle materials: Computation and extreme value theory. In 5th Int. Conference FraMCoS – Fracture Mechanics of Concrete and Concrete Structures (Vail, Colorado, USA, 2004), V. C. Li, K. J. Willam, C. K. Y. Leung, and S. L. Billington, Eds., vol. 1, Ia-FraMCos, pp. 189–196. ˇ ´ , M. Performance study of correlation control in Monte Carlo type sim[41] VO RECHOVSK Y ulation. In 3rd PhD Workshop Brno-Prague-Weimar, also ISM-Bericht 1/2006 BauhausUniverist¨at Weimar (Weimar, Germany, 2006), pp. 35–38. ˇ ´ , M., BA Zˇ ANT, Z. P., AND N OV AK ´ , D. Procedure of statistical size effect [42] VO RECHOVSK Y prediction for crack initiation problems. In ICF XI 11th International Conference on Fracture (Turin, Italy, 2005), A. Carpinteri, Ed., Politecnico di Torino, pp. CD–ROM proc, abstract page 1166. ˇ ´ , M., AND C HUDOBA , R. Statistical length scale for micromechanical [43] VO RECHOVSK Y model of multifilament yarns and size effect on strength. In ICoSSaR ’05 the 9 th International Conference on Structural Safety and Reliability (Rome, Italy, 2005), G. Augusti, G. I. Schu¨eller, and M. Ciampoli, Eds., Millpress Rotterdam, Netherlands, pp. 395–401. ˇ ´ , M., C HUDOBA , R., AND J E Rˇ ABEK ´ [44] VO RECHOVSK Y , J. Adaptive probabilistic modeling of localization, failure and size effect of quasi-brittle materials. In III European Conference on Computational Mechanics (ECCM-2006) (Lisbon, Portugal, 2006), C. Soares, J. Martins, H. Rodrigues, J. Ambr´osio, C. Pina, C. Soares, E. Pereira, and J. Folgado, Eds., National Laboratory of Civil Engineering, Springer, p. 286 (abstract). Full papers on CD-ROM. ˇ ´ , M., AND M ATESOV A´ , D. Interplay of sources of size effects in concrete [45] VO RECHOVSK Y specimens. In Fracture of Nano and Engineering Materials and Structures, Proceedings of the 16th European Conference of Fracture (Alexandroupolis, Greece, 2006), E. E. Gdoutos, Ed., Springer, pp. 1365–1366 (Abstract), full length paper on CD–ROM. ˇ ´ , M., AND N OV AK ´ , D. Correlated random variables in probabilistic simu[46] VO RECHOVSK Y lation. In 4th International Ph.D. Symposium in Civil Engineering (Munich, Germany, 2002), P. Schießl, N. Gebbeken, M. Keuser, and K. Zilch, Eds., vol. 2, Millpress, Rotterdam, pp. 410– 417. 39
ˇ ´ , M., AND N OV AK ´ , D. Efficient random fields simulation for stochastic [47] VO RECHOVSK Y nd FEM analyses. In 2 M.I.T. Conference on Computational Fluid and Solid Mechanics (Cambridge, USA, 2003), K. J. Bathe, Ed., Elsevier Science Ltd., Oxford, UK, pp. 2383–2386. ˇ ´ , M., AND N OV AK ´ , D. Statistical correlation in stratified sampling. In [48] VO RECHOVSK Y ICASP 9, International Conference on Applications of Statistics and Probability in Civil Engineering (San Francisco, USA, 2003), A. Der Kiureghian, S. Madanat, and J. M. Pestana, Eds., Millpress, Rotterdam, pp. 119–124. ˇ ´ , M., AND N OV AK ´ , D. Simulation of random fields for stochastic finite [49] VO RECHOVSK Y element analyses. In ICoSSaR ’05 the 9 th International Conference on Structural Safety and Reliability (Rome, Italy, 2005), G. Augusti, G. I. Schu¨eller, and M. Ciampoli, Eds., Millpress Rotterdam, Netherlands, pp. 2545–2552.
Summary in Czech Pˇredloˇzen´a pr´ace shrnuje v´ysledky dosaˇzen´e autorem bˇehem posledn´ıch pˇeti let a kter´e jsou podrobnˇe rozvedeny v habilitaˇcn´ı pr´aci autora. Nov´e pˇr´ınosy lze spatˇrovat v nˇekolika oblastech: (1) simulace n´ahodn´ych veliˇcin a vektor˚u typu Monte Carlo se zamˇeˇren´ım na statistickou korelaci cˇ i jin´e typy z´avislost´ı mezi veliˇcinami; (2) simulace n´ahodn´ych pol´ı v kontextu stochastick´e metody koneˇcn´ych prvk˚u a zamˇeˇren´ı na posouzen´ı pˇresnosti simulovan´ych vzork˚u s ohledem na r˚uzn´e metody pouˇzit´e pˇri simulaci; v´yvoj metody pro u´ cˇ innou simulaci neGaussovsk´ych n´ahodn´ych pol´ı; (3) n´avrh nov´e a u´ cˇ inn´e metody pro simulaci vz´ajemnˇe korelovan´ych neGaussovsk´ych n´ahodn´ych pol´ı; (4) v´yvoj mikromechanick´eho modelu zatˇezˇ ov´an´ı svazku vl´aken pouˇzit´e-ho jako v´yztuˇz do textilem vyztuˇzen´eho betonu; zde se autor zamˇeˇril na v´yvoj teorie vlivu d´elky svazku na pevnost ovlivnˇenou r˚uzn´ymi zdroji n´ahodnosti a prostorov´e promˇenlivosti materi´alov´ych parametr˚u, autorem byly navrˇzeny procedury a vztahy pro podchycen´ı tˇechto vliv˚u a zejm´ena pro zaveden´ı d´elkov´eho mˇerˇ´ıtka do klasick´e Weibullovy teorie pevnosti s vysvˇetlen´ım proˇc je rozˇs´ıˇren´ı teorie nutn´e (5) vlivem velikosti betonov´ych konstrukc´ı na jejich u´ nosnost; zde je pˇredstavena nov´a metoda pro podchycen´ı statistick´e sloˇzky vlivu velikosti podloˇzen´a teori´ı extr´emn´ıch hodnot a d´ale je sledov´an komplexn´ı vliv velikosti (statistick´a i deterministick´a sloˇzka a jejich interakce). Tato interakce je podrobnˇe studov´ana (6) spolu s vlivem okraj˚u za pomoc´ı modern´ıch prostˇredk˚u neline´arn´ı stochastick´e anal´yzy a porovn´av´ana s ojedinˇel´ymi testy na betonov´ych tr´amc´ıch tvaru ps´ı kosti (pomˇer velikost´ı 1:32); (7) v t´eto oblasti vedl v´yzkum k navrˇzen´ı a ovˇeˇren´ı nov´eho vztahu pro komplexn´ı vliv velikosti a uk´azkou aplikace pˇri anal´yze kolapsu pˇrehrady Malpasset; (8) zkuˇsenosti s neline´arn´ı stochastickou mechanikou motivovaly autora k zapoˇcet´ı v´yvoje v´ypoˇctov´e platformy pro adaptivn´ı metodu koneˇcn´ych prvk˚u rˇ´ızenou n´ahodnou prostorovou promˇenlivost´ı parametr˚u modelu; (9) autor se dlouhodobˇe zab´yv´a v´yvojem software a je autorem v´ypoˇctov´eho j´adra softwaru FReET.
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
Al safetyPříloha 12 Autoevaluační hodnocení uchazeče na základě požadavků schválených VR VUT v Brně Autoevaluační kritéria podle čl. 2 odst. 2 písm. c) Směrnice VUT v Brně pro habilitační řízení a řízení ke jmenování profesorem – minimální poţadavky k podání ţádosti o titul „doc.― uchazečem a dosaţené počty bodů ke dni 21. 5. 2007: Kategorie
A1 – A6
A7 – A14
A ostatní
A celkem
B celkem
Poţadavek Dosaženo
50 97.5 +54=151.5
50 307 +76=383
40 50
140 585
40 50
Celkem A+B 180 635
Pozn.: Čísla za operátorem ―+‖ značí body za citace (uznání vědeckou a odbornou komunitou). Citace v kategorii A6 zahrnují pouze citace v impaktovaných mezinárodních časopisech nebo State-of-the-art reportech světové organizace RILEM. Ostatní citace (z konferenčních článků apod. jsou započteny v kategorii A14).
V případě, ţe na konferenci byl publikován jak článek, tak abstrakt (případně rozšířený abstrakt), je uveden a započten pouze článek.
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A. Vědecká a odborná činnost 1) Tvůrčí a publikační činnost Práce před rokem udělení vědecké hodnosti Ph.D. Diplomová práce (1t) VOŘECHOVSKÝ, M. (2000). K problematice výpočtu spolehlivosti u nelineárních úloh mechaniky kontinua. Diplomová práce, Ústav stavební mechaniky, Fakulta stavební, Vysoké učení technické v Brně. V češtině. nebodováno Technické zprávy, programová dokumentace (1r) VOŘECHOVSKÝ, M., ČERVENKA, V. (2002). ATENA 2D - Uživatelský manuál. Červenka Consulting, Prague, Czech Republic. A25: 2/2 = 1 b. NOVÁK, D., VOŘECHOVSKÝ, M., RUSINA, R., LEHKÝ, D., TEPLÝ, B., KERŠNER, Z. (2002). FReET – Feasible REliability Engineering Efficient Tool. Institute of Engineering Mechanics, Faculty of Civil Engineering, Brno University of Technology/Červenka Consulting, Praha, Czech Republic, program documentation. A25: 2/2 = 1 b. Publikace na zahračních konferencích (vyjma SR) (2r)
2001 (1z)
NOVÁK, D., VOŘECHOVSKÝ, M., PUKL, R., ČERVENKA, V. (2001). Statistical nonlinear analysis – size effect of concrete beams. In: de Borst, R. et al. (Eds.), 4th Int. Conference FraMCoS – Fracture Mechanics of Concrete and Concrete Structures. Swets & Zeitlinger, Lisse. Cachan, France, pp. 823–830. ISBN 90 2651 825-0. A9: 10/2 = 5 b.
(2z)
KALA, Z., NOVÁK, D., VOŘECHOVSKÝ, M. (2001). Probabilistic nonlinear analysis of steel frames focused on target reliability of Eurocodes. In: Corotis, R. et al. (Eds.), ICoSSaR ’01– 8th International Conference on Structural Safety and Reliability. A.A.Balkema Publishers, Netherlands. Newport Beach, California, USA, p. 146. ISBN 90 5809 197. A9: 10/2 = 5 b.
2002 (7z)
NOVÁK, D., PUKL, R., VOŘECHOVSKÝ, M., RUSINA, R., ČERVENKA, V. (2002). Structural reliability assessment of computationally intensive problems nonlinear FEM analysis. In: Das, P. (Ed.),1st International ASRANet Colloquium. Glasgow, Scotland, pp. CD – ROM proc. A9: 10/2 = 5 b.
(8z)
VOŘECHOVSKÝ, M., PUKL, R., VESELÝ, V., ČERVENKA V., RUSINA, R. (2002). Statistical nonlinear analysis of concrete structures. In: Dhir, R., Jones, M.
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Ing. Miroslav Vořechovský, Ph.D.
R., Zheng, L. (Eds.), International Congress on Challenges of Concrete Construction, Seminar 3. Dundee, Scotland, pp. 217–226. ISBN: 0 7277 3175 0. A9: 10/2 = 5 b. (9z)
VOŘECHOVSKÝ, M., NOVÁK, D. (2002). Correlated random variables in probabilistic simulation. In: Schießl, P. et al. (Eds.), 4th International Ph.D. Symposium in Civil Engineering. Vol. 2. Millpress, Rotterdam. Munich, Germany, pp. 410– 417. Awarded paper. ISBN 3-935065-09-4. A9: 10/2 = 5 b.
2003 (10z) VOŘECHOVSKÝ, M., NOVÁK, D. (2003). Efficient random fields simulation for stochastic FEM analyses. In: Bathe, K. (Ed.), 2nd M.I.T. Conference on Computational Fluid and Solid Mechanics. Elsevier Science Ltd., Oxford, UK. Cambridge, USA, pp. 2383–2386. Awarded paper. ISBN: 0-08-044046-0. A9: 10/2 = 5 b. (11z) NOVÁK, D., VOŘECHOVSKÝ, M., RUSINA, R. (2003). Small-sample probabilistic assessment – FREET software. In: Der Kiureghian et al. (Eds.), ICASP 9, Int. Conference on Applications of Statistics and Probability in Civil Engineering. Millpress, Rotterdam, San Francisco, USA, pp. 91–96. ISBN 90 5966 004 8. A9: 10/2 = 5 b. (12z) VOŘECHOVSKÝ, M., NOVÁK, D. (2003). Statistical correlation in stratified sampling. In: Der Kiureghian et al. (Eds.), ICASP 9, International Conference on Applications of Statistics and Probability in Civil Engineering. Millpress, Rotterdam. San Francisco, USA, pp. 119–124. ISBN 90 5966 004 8. A9: 10/2 = 5 b. (13z) NOVÁK, D., BAŢANT, Z. P., VOŘECHOVSKÝ, M. (2003). Computational modeling of statistical size effect in quasibrittle structures. In: Der Kiureghian et al. (Eds.), ICASP 9, International Conference on Applications of Statistics and Probability in Civil Engineering. Millpress, Rotterdam. San Francisco, USA, pp. 621– 628. ISBN 90 5966 004 8. A9: 10/2 = 5 b. Publikace na konferencích v České a Slovenské Republice 2000 (1c)
VOŘECHOVSKÝ, M. (2000). Probabilistic analysis of steel frames focused on design concept of Eurocodes. In: Workshop 3RE (on CD ROM). Institute of Structural mechanics, FCE, BUT Brno, Brno, Czech Republic. A11: 4/1 = 4 b.
(2c)
VOŘECHOVSKÝ, M., NOVÁK, D., PUKL, R., ČERVENKA, V. (2000). Modelování vlivu velikosti nosníku na jeho únosnost prostředky nelineární lomové mechaniky — SBETA/ATENA. In: Betonářské Dny 2000. ČBZ, Pardubice, Czech Republic, pp. 375–379. A11: 4/2 = 2 b.
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2001 (3z)
VOŘECHOVSKÝ, M., NOVÁK, D. (2001). Probability of failure using reliability design concept of Eurocodes. In: TRANSCOM ’2001 – 4th European Conference of Young Research and Science Workers in Transport and Telecommunications. EDIS Press, Ţilina, Slovak Republic. ISBN 80-7100-852-4. A11: 4/2 = 2 b.
(3c)
VOŘECHOVSKÝ, M. (2001). Modelování vlivu velikosti na pevnost v tahu za ohybu u betonových nosníků. In: 3rd Scientific international PhD workshop. Brno University of Technology, Faculty of Civil Engineering, Brno, Czech Republic, pp. 145–148. ISSN 1212-9275. A11: 4/1 = 4 b.
(4c)
VOŘECHOVSKÝ, M. (2001). Pravděpodobnostní posouzení ocelové konstrukce podle EC 1. In: 3rd Scientific international PhD workshop. Brno University of Technology, Faculty of Civil Engineering, Brno, Czech Republic, pp. 141–144. ISSN 1212-9275. A11: 4/1 = 4 b.
(5c)
VOŘECHOVSKÝ, M., KALA, Z. (2001). Nevyrovnanost pravděpodobnosti poruchy podle konceptu EC 1. In: Reliability of Structures, 2nd International Conference, Faculty of Civil Engineering, Ostrava University of Technology. Academy of Sciences – Institute of Theoretical and Applied Mechanics of the ASCR Prague, Ostrava, Czech Republic, pp. 141–144. ISBN 80-02-01410-3. A11: 4/2 = 2 b.
(6c)
VOŘECHOVSKÝ, M. (2001). Moţnosti vyuţití lomové mechaniky v kombinaci se stochastickými metodami při modelováni vlivu velikosti. In: Stibor (Ed.), Problémy lomové mechaniky. Brno University of Technology, Academy of Sciences - Institute of physics of materials of the ASCR, pp. 93–97. ISBN 80-2141906-7. A11: 4/1 = 4 b.
(7c)
PUKL, R., NOVÁK, D., VOŘECHOVSKÝ, M., RUSINA, R., ČERVENKA, V. (2001). Stochastická nelineární analýza betonových konstrukcí. In: Betonářské Dny 2001. ČBZ, Pardubice, Czech Republic, pp. 136–141. ISBN 80-238-7595-7. A11: 4/2 = 2 b.
2002 (4z)
VESELÝ, V., KERŠNER, Z., STIBOR, M., VOŘECHOVSKÝ, M. (2002). Efektivní hodnota lomové houţevnatosti z krychle se zářezy. In: Kmeť, S., Krištofovič, V. (Eds.), VII International Scientific Conference, Section 9: Structural Mechanics. Košice (Medzev), Slovak Republic, pp. 300–304. ISBN 80-7099-815-6. A11: 4/2 = 2 b.
(5z)
VOŘECHOVSKÝ, M., VESELÝ, V., PUKL, R. (2002). Statistical nonlinear analysis of concrete structures. In: Kmeť, S., Krištofovič, V. (Eds.), VII International Scientific Conference, Section 9: Structural Mechanics. Technical University of Košice, Košice (Medzev), Slovak Republic, pp. 308–313. ISBN 80-7099-815-6.
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A11: 4/2 = 2 b. (6z)
VOŘECHOVSKÝ, M., NOVÁK, D.AND RUSINA, R. (2002). A new efficient technique for samples correlation in Latin Hypercube Sampling. In: Kmeť, S., Miron, P. (Eds.), VII International Scientific Conference, Section 1: Applied Mathematics. Technical University of Košice, Košice, Slovak Republic, pp. 102–108. ISBN 80-7099-808-3. A11: 4/2 = 2 b.
(8c)
VOŘECHOVSKÝ, M. (2002). Nové úpravy simulační metody Latin Hypercube Sampling a moţnosti vyuţití. In: Stibor, M. (Ed.), Problémy modelování. Brno University of Technology, Faculty of Civil Engineering VšB-TUO, Ostrava, Czech Republic, pp. 83–90. ISBN 80-214-2017-0. A11: 4/1 = 4 b.
(9c)
VOŘECHOVSKÝ, M. (2002). Moţnosti a vylepšení simulační metody LHS. In: 4rd Scientific PhD international workshop. Brno University of Technology, pp. CD–ROM. ISBN-80-214-2067-7. A11: 4/1 = 4 b.
(10c) VOŘECHOVSKÝ, M. (2002). FEM simulace předpjatých nosníků a jejich statistické zpracování. In: 4rd Scientific PhD international workshop. Brno University of Technology, CD–ROM. ISBN-80-214-2067-7. A11: 4/1 = 4 b. (11c) LEHKÝ, D., VOŘECHOVSKÝ, M. (2002). Generování náhodných polí: Teorie a aplikace. In: 4rd Scientific PhD international workshop. Brno University of Technology, CD–ROM. ISBN-80-214-2067-7. A11: 4/2 = 2 b. (12c) NOVÁK, D., RUSINA, R., VOŘECHOVSKÝ, M. (2002). Freet - software pro pravděpodobnostní posudky výpočtově náročných problémů mechaniky kontinua. In: Reliability of Structures, 3rd International Conference, Faculty of Civil Engineering, Ostrava University of Technology. Academy of Sciences – Institute of Theoretical and Applied Mechanics of the ASCR Prague. Ostrava, Czech Republic, pp. 71–74. ISBN 80-02-01489-8. A11: 4/2 = 2 b. (13c) VOŘECHOVSKÝ, M. (2002). Lomově-mechanické parametry betonu jako vzájemně korelovaná náhodná pole. In: Stibor (Ed.), Problémy lomové mechaniky II. Brno University of Technology, Academy of Sciences - Institute of physics of materials of the ASCR, pp. 84–90. ISBN 80-214-2129-0. A11: 4/1 = 4 b. (14c) NOVÁK, D., PUKL, R., RUSINA, R., VOŘECHOVSKÝ, M., ČERVENKA, V. (2002). Statistical, sensitivity and reliability assessment of computationally intensive problems: Nonlinear fracture mechanics analysis. In: Menčík. (Ed.), Reliability and Diagnostic of Transport Structures and Means 2002, First International Conference. University of Pardubice, CR, pp. 252–260. ISBN 80-7194-464-5. A11: 4/2 = 2 b.
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Ing. Miroslav Vořechovský, Ph.D.
2003 (15c) VOŘECHOVSKÝ, M. (2003). Application of extreme value theory for size effect of concrete structures. In: 5th Scientific international PhD workshop. Brno University of Technology, Faculty of Civil Engineering, Brno, Czech Republic, CD-ROM proc. ISBN 80-7204-265-3. A11: 4/1 = 4 b. (16c) LEHKÝ, D., MATESOVÁ, D., NOVÁK, D., TEPLÝ, B., VESELÝ, V., VOŘECHOVSKÝ, M. (2003). Stochastic analysis, fracture mechanics and pre-stressed concrete members. In: Konvalinka, P., Máca, J. (Eds.), CTU Reports 1 Vol 7, 2003: Contribution to Computational and Experimental Investigation of Engineering Materials and Structures. Dedicated to Zdeněk Bittnar on the occasion of his 60th birthday. Czech Technical University in Prague, Faculty of Civil Engineering, Prague, Czech Republic, pp. 111–120. ISBN 80-01-02734-1. A11: 4/2 = 2 b. (17c) PERLA, J., VOŘECHOVSKÝ, M. (2003). Vyuţití programu FEAT při rekonstrukcích. In: Statika 2003. Hotel Myslivna, Brno, Czech Republic. A11: 4/2 = 2 b. Práce v a po roce udělení vědecké hodnosti Ph.D. Doktorská dizertační práce (2t) VOŘECHOVSKÝ, M. (2004). Stochastic fracture mechanics and size effect. Ph.D. thesis, Vysoké učení technické v Brně. ISBN 80-214-2695-0. V angličtině. nebodováno Články v recenzovaných časopisech (1j)
CHUDOBA, R., VOŘECHOVSKÝ, M., KONRAD, M. (2006). Stochastic modeling of multi-filament yarns I: Random properties within the cross section and size effect. International Journal of Solids and Structures (Elsevier), (3-4), 413–434. ISSN: 0020-7683. (Impact Factor=1.3) A2: 20/2 = 10 b.
(2j)
VOŘECHOVSKÝ, M., CHUDOBA, R. (2006). Stochastic modeling of multifilament yarns II: Random properties over the length and size effect. International Journal of Solids and Structures (Elsevier), 43 (3-4), 435–458. ISSN: 0020-7683. (Impact Factor=1.3) A2: 20/2 = 10 b.
(3j)
VOŘECHOVSKÝ, M. (2006). Size effects in concrete specimens studied via stochastic fracture mechanics. Engineering mechanics (Inženýrská mechanika), 13 (5), 385–401, ISSN 1210-2717. (Impact Factor není znám) A4: 10/1 = 10 b.
(4j)
BAŢANT, Z. P., VOŘECHOVSKÝ, M., NOVÁK, D. (2007). Asymptotic prediction of energetic-statistical size effect from deterministic finite element solutions. Journal of Engineering Mechanics (ASCE), 133(2), 153–162, ISSN 0733-9399. (Impact Factor=0.832) A2: 20/2 = 10 b.
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(5j)
VOŘECHOVSKÝ, M. (2007). Interplay of size effects in concrete specimens under tension studied via computational stochastic fracture mechanics. International Journal of Solids and Structures (Elsevier), 44 (9), 2715–2731. ISSN: 0020-7683. http://dx.doi.org/10.1016/j.ijsolstr.2006.08.019 (Impact Factor=1.3) A2: 20/1 = 20 b.
(6j)
BAŢANT, Z. P., PANG, S. D., VOŘECHOVSKÝ, M., NOVÁK, D. (2007). Energetic-Statistical Size Effect Simulated by SFEM with Stratified Sampling and Crack Band Model. International Journal of Numerical Methods in Engineering (John Wiley & Sons, Ltd), in press. ISSN 0029-5981. (Impact Factor=1.2) A2: 20/2 = 10 b.
(7j)
VOŘECHOVSKÝ, M., CHUDOBA, R., HANISCH, V., GRIES, T. (2007). Effect of twist, fineness, loading rate and length on tensile behavior of multifilament yarns (a multivariate study). Textile Research Journal (Sage), accepted, in print. ISSN 0266-8920. (Impact Factor=0.5) A3: 15/1 = 7.5 b.
(8j)
VOŘECHOVSKÝ, M. (2007). Simulation of simply cross correlated random fields by series expansion methods. Structural safety (Elsevier), accepted, in print. ISSN 0167-4730. (Impact Factor=0.953) A2: 20/1 = 20 b.
(9j)
CHUDOBA, R., KONRAD, VOŘECHOVSKÝ, M., ROYE, A. (2007). Thin fiber and textile reinforced cementitious systems (SP-244). ACI Special publication of the American Concrete Institute (ACI), Farmington Hills, MI, Chapter: Numerical and experimental study of imperfections in the yarn and its bond to cementitious matrix, pp. electronic book on CD—ROM. ISBN 978-0-87031-230-4. ještě nebodováno
Technické zprávy, programová dokumentace (3r)
NOVÁK, D., VOŘECHOVSKÝ, M., RUSINA, R.., LEHKÝ, D, (2004). FReET– Part 1 – Theory. Tech. rep., Červenka Consulting / Brno, www.freet.cz. Czech Republic, program documentation. A25: 2/2 = 1 b.
(4r)
LEHKÝ, D., NOVÁK, D., KERŠNER, Z., VOŘECHOVSKÝ, M., MATESOVÁ, D., ŘOUTIL, L. (2005). Identifikace parametrů výpočtových modelů pomocí stochastické simulace a neuronových sítí, Cideas – centrum integrovaného navrhování progresivních stavebních konstrukcí: Dílčí výzkumná zpráva za rok 2005. Tech. rep., Institute of Engineering Mechanics, Faculty of Civil Engineering, Brno University of Technology, in Czech. A25: 2/2 = 1 b.
(5r)
NOVÁK, D., VOŘECHOVSKÝ, M. (2006). FReET– Part 1 – Theory. Tech. rep., Červenka Consulting / Brno, www.freet.cz. Czech Republic, program documentation. A25: 2/2 = 1 b.
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(6r)
NOVÁK, D., VOŘECHOVSKÝ, M., RUSINA, R. 2006. FReET – Part 2 – User manual. Tech. rep., Červenka Consulting / Brno, www.freet.cz. Czech Republic, program documentation. A25: 2/2 = 1 b.
(7r)
VOŘECHOVSKÝ, M., ŘOUTIL, L. 2006. Stanovení lomově-mechanických vlastností hutných a lehčených kompozitních materiálů – zpráva o řešení za rok 2006. Tech. rep., Červenka Consulting / Brno, www.freet.cz. Czech Republic, program documentation. A25: 2/2 = 1 b.
Publikace na zahračních konferencích (vyjma SR) 2004 (14z) BAŢANT, Z. P., PANG, S. D., VOŘECHOVSKÝ, M., NOVÁK, D.AND PUKL, R. (2004). Statistical size effect in quasibrittle materials: Computation and extreme value theory. In: Li, V. C. et al. (Eds.), 5th Int. Conference FraMCoS – Fracture Mechanics of Concrete and Concrete Structures. Vol. 1. Ia-FraMCos. Vail, Colorado, USA, pp. 189–196. ISBN 0 87031 135 2. A9: 10/2 = 5 b. (15z) VOŘECHOVSKÝ, M., NOVÁK, D. (2004). Modeling statistical size effect in concrete by the extreme value theory. In: Walraven, J. et al. (Eds.), 5th International Ph.D. Symposium in Civil Engineering. Vol. 2. A.A. Balkema Publishers, London. Delft, The Netherlands, pp. 867–875. ISBN 90 5809 676 9. A9: 10/2 = 5 b. 2005 (16z) VOŘECHOVSKÝ, M., BAŢANT, Z. P.AND NOVÁK, D. (2005). Procedure of statistical size effect prediction for crack initiation problems. In: Carpinteri, A. (Ed.), ICF XI 11th International Conference on Fracture. Politecnico di Torino, Turin, Italy, pp. CD–ROM proc, abstract page 1166. A9: 10/2 = 5 b. (17z) CHUDOBA, R., KONRAD, M., MOMBARTZ, M., VOŘECHOVSKÝ, M., MESKOURIS, K. (2005). Multiscale modeling of textile reinforced concrete within a consistent modeling framework. In: Ramm, E., Wall, W. A., Bletzinger, K.-U., Bischoffi, M. (Eds.), 5th International Conference on Computation of Shell and Spatial Structures. CD-ROM proc., Salzburg, Austria, pp. 1–4. A9: 10/2 = 5 b. (18z) VOŘECHOVSKÝ, M., NOVÁK, D. (2005). Simulation of random fields for stochastic finite element analyses. In: Augusti, Schuëller and Ciampoli (Eds.), ICoSSaR ’05 the 9th International Conference on Structural Safety and Reliability. Millpress Rotterdam, Netherlands, Rome, Italy, pp. 2545–2552. ISBN 90-5966-040-4. A9: 10/2 = 5 b. (19z) BAŢANT, Z. P., VOŘECHOVSKÝ, M., NOVÁK, D. (2005). Role of deterministic and statistical length scales in size effect for quasibrittle failure at crack initia-
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Ing. Miroslav Vořechovský, Ph.D.
tion. In: Augusti, Schuëller and Ciampoli (Eds.), ICoSSaR ’05 the 9th International Conference on Structural Safety and Reliability. Millpress Rotterdam, Netherlands, Rome, Italy, pp. 411–415. ISBN 90-5966-040-4. A9: 10/2 = 5 b. (20z) VOŘECHOVSKÝ, M., CHUDOBA, R. (2005). Statistical length scale for micromechanical model of multifilament yarns and size effect on strength. In: Augusti, Schuëller and Ciampoli (Eds.), ICoSSaR ’05 the 9th International Conference on Structural Safety and Reliability. Millpress Rotterdam, Netherlands, Rome, Italy, pp. 395–401. ISBN 90-5966-040-4. A9: 10/2 = 5 b. (21z) NOVÁK, D., VOŘECHOVSKÝ, M., LEHKÝ, D., RUSINA, R., PUKL, R., ČERVENKA, V. 2005. Stochastic nonlinear fracture mechanics finite element analysis of concrete structures. In: Augusti, Schuëller and Ciampoli (Eds.), ICoSSaR ’05 the 9th International Conference on Structural Safety and Reliability. Millpress Rotterdam, Netherlands, Rome, Italy, pp. 781–788. ISBN 90-5966-040-4. A9: 10/2 = 5 b. (22z) PUKL, R., ČERVENKA, J., ČERVENKA, V., NOVÁK, D., VOŘECHOVSKÝ, M., LEHKÝ, D. (2005). Deterministic and statistical models for nonlinear FE analysis of FRC-based structures. In: Pauser, M. (Ed.), 1st Central European Congress on Concrete Engineering: Fibre Reinforced Concrete in Practice. Austrian society for concrete and construction technology, Graz, Austria, pp. 130–133. A9: 10/2 = 5 b. (23z) NOVÁK, D., KERŠNER, Z., LEHKÝ, D., ŘOUTIL, L., VOŘECHOVSKÝ, M., KNĚZEK, J., PUKL, R. (2005). Virtual stochastic simulation of fiber reinforced concrete experiments. In: Pauser, M. (Ed.), 1st Central European Congress on Concrete Engineering: Fibre Reinforced Concrete in Practice. Austrian society for concrete and construction technology, Graz, Austria, pp. 35–38. A9: 10/2 = 5 b. (25z) NOVÁK, D., VOŘECHOVSKÝ, M., RUSINA, R. (2005). Small Sample Simulation Methods for Statistical, Sensitivity and Reliability Analyses. In: Bergmeister, K., Strauss, A., Rickenmann, D. (Eds.), Proc. of the 3rd Probabilistic Workshop: Technical Systems + Natural Hazards, also Schriftenreihe des Departments Nr. 7 November 2005. Universität für Bodenkultur Wien, Department für Bautechnik + Naturgefahren, Vienna, Austria, pp. 51–59, ISSN 1811-8747. A9: 10/2 = 5 b. 2006 (26z) VOŘECHOVSKÝ, M., MATESOVÁ, D. (2006). Size effect in concrete specimens under tension: interplay of sources. In: Meschke, de Borst, Mang, Bičanič (Eds.), EURO-C 2006 Computational Modelling of Concrete Structures. Taylor & Francis Group, London, Mayrhofen, Austria, pp. 905–914. ISBN: 0 415 39749 9. A9: 10/2 = 5 b. (27z) PUKL, R., JANSTA, M., ČERVENKA, J., VOŘECHOVSKÝ, M., NOVÁK, D., RUSINA, R. 2006. Spatial variability of material properties in advanced nonlinear
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computer simulation. In: Meschke, de Borst, Mang, Bičanič (Eds.), EURO-C 2006 Computational Modelling of Concrete Structures. Taylor & Francis Group, London, Mayrhofen, Austria, pp. 891–896. ISBN: 0 415 39749 9. A9: 10/2 = 5 b. (28z) KONRAD, M., JEŘÁBEK, J., VOŘECHOVSKÝ, M., CHUDOBA, R. (2006). Evaluation of mean performance of cracks bridged by multi-filament yarns. In: Meschke, de Borst, Mang, Bičanič (Eds.), EURO-C 2006 Computational Modelling of Concrete Structures. Taylor & Francis Group, London, Mayrhofen, Austria, pp. 873–880. ISBN: 0 415 39749 9. A9: 10/2 = 5 b. (29z) MATESOVÁ, D., VOŘECHOVSKÝ, M. (2006). Mechanical parameters for numerical modeling of concrete at high temperatures. In: 3rd PhD Workshop BrnoPrague-Weimar, also ISM-Bericht 1/2006 Bauhaus-Univeristät Weimar. Weimar, Germany, pp. 17–18, ISSN 1610-7381. A9: 10/2 = 5 b. (30z) VOŘECHOVSKÝ, M. (2006). Performance study of correlation control in Monte Carlo type simulation. In: 3rd PhD Workshop Brno-Prague-Weimar, also ISMBericht 1/2006 Bauhaus-Univeristät Weimar. Weimar, Germany, pp. 35–38. ISSN 1610-7381. A9: 10/1 = 10 b. (31z) VOŘECHOVSKÝ, M., CHUDOBA, R., JEŘÁBEK, J. (2006). Adaptive probabilistic modeling of localization, failure and size effect of quasi-brittle materials. In: Soares et al. (Eds.), III European Conference on Computational Mechanics (ECCM-2006). National Laboratory of Civil Engineering, Springer, Lisbon, Portugal, p. 286 (abstract), full papers on CD-ROM. ISBN 1-4020-4994-3. A9: 10/2 = 5 b. (32z) VOŘECHOVSKÝ, M., MATESOVÁ, D. (2006). Interplay of sources of size effects in concrete specimens. In: Gdoutos, E. E. (Ed.), Fracture of Nano and Engineering Materials and Structures, Proceedings of the 16th European Conference of Fracture. Springer, Alexandroupolis, Greece, pp. 1365–1366 (Abstract), full length paper on CD–ROM. ISBN: -13 978-1-4020-4971-2. A9: 10/2 = 5 b. (33z) CHUDOBA, R., VOŘECHOVSKÝ, M., JEŘÁBEK, J., KONRAD, M. (2006). TRC-specimens modeled as a chain of cracks bridged by bundles: Study of impact of local scatter on global tensile strength. In: Konsta-Gdoutos, M.~S. (Ed.), Measuring, Monitoring and Modeling Concrete Properties. (International Symposium dedicated to Prof. Surendra. P. Shah) Springer, Dotrecht, the Netherlands. Alexandroupolis, Greece, pp. 777—783. ISBN: 1-4020-5103-4. A9: 10/2 = 5 b. (34z) VOŘECHOVSKÝ, M., JEŘÁBEK, J., CHUDOBA, R., 2006. Impact of scatter of material properties on the yarn performance in TRC. In: Hegger, J., Brameshuber, W., Will, N. (Eds.), 1st International RILEM Conference on Textile Reinforced
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Ing. Miroslav Vořechovský, Ph.D.
Concrete. RWTH Aachen University, Germany, pp. 285–294. ISBN: 2-912143-977. A9: 10/2 = 5 b. (35z) PUKL, R., NOVÁK, D., VOŘECHOVSKÝ, M., BERGMEISTER, K., 2006. Uncertainties of material properties in nonlinear computer simulation. In: Proske, D., Mehdiapour, M., Gucma, L. (Eds.), 4th International Probabilistic Symposium. BAM Berlin, Germany, pp. 127–138. ISBN:-10: 3-00-019232-8, -13: 978-3-00019232-6. A9: 10/2 = 5 b. (36z) VOŘECHOVSKÝ, M., KONRAD, M., CHUDOBA, R. (2006). Multiple cracks bridged by multifilament yarns: impact of local scatter on ultimate load. In: Brandt, A., Li, V., Marshall, I. (Eds.), BMC8 Eighth International Symposium on Brittle Matrix Composites. Woodhead Publishing Limited, Cambridge. Warsaw, Poland, pp. 361–372. ISBN: 1-84569-031-1, 83-89687-09-7. A9: 10/2 = 5 b. 2007 (37z) VOŘECHOVSKÝ, M. (2007). Statistical length scale in the Weibull strength theory and its interaction with other scaling lengths in quasibrittle failure. In: IUTAM Symposium on Scaling in Solid Mechanics. Organization: International Union of Theoretical and Applied Mechanics (IUTAM). Cardiff, UK. June 25 - June 29, 2007 p. in print. A9: 10/1 = 10 b. (38z) VOŘECHOVSKÝ, M. (2007). Simulation of simply cross correlated random fields by series expansion methods. In: ICASP 10, 10th International Conference on Applications of Statistics and Probability in Civil Engineering. The University of Tokyo, Kashiwa Capmus. The University of Tokyo, Tokyo, Japan, p. in print. A9: 10/2 = 5 b. (39z) VOŘECHOVSKÝ, M. (2007). Modeling statistical size effect in quasibrittle materials by computational stochastic fracture mechanics. In: ICASP 10, 10th International Conference on Applications of Statistics and Probability in Civil Engineering. The University of Tokyo, Kashiwa Capmus. The University of Tokyo, Tokyo, Japan, p. in print. A9: 10/1 = 10 b. (40z) NOVÁK, D., PUKL, R., BERGMEISTER, K., VOŘECHOVSKÝ, M., LEHKÝ, D., ČERVENKA, V. 2007. Stochastic nonlinear analysis of concrete structures Part I: From simulation of experiment and parameters identification to reliability assessment. In: ICASP 10, 10th International Conference on Applications of Statistics and Probability in Civil Engineering. The University of Tokyo, Kashiwa Capmus, The University of Tokyo, Tokyo, Japan, p. in print. A9: 10/1 = 10 b. Publikace na konferencích v České a Slovenské Republice
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2004 (18c) VOŘECHOVSKÝ, M. (2004). Statistical alternatives of combined size effect on nominal strength for structures failing at crack initiation. In: Stibor, M. (Ed.), Problémy lomové mechaniky IV. Brno University of Technology, Academy of Sciences - Institute of physics of materials of the ASCR, pp. 99–106. ISBN 80-2142585-7. A11: 4/1 = 4 b. (19c) VOŘECHOVSKÝ, M., CHUDOBA, R. (2004). Stochastické modelování mnohovláknitých svazků pro vyztuţení betonu. In: PPK 2004 Pravděpodobnost porušování konstrukcí. Brno University of Technology, Faculty of Civil Engineering & ústav aplikované mechaniky Brno, s.r.o. & Asociace strojních inţenýrů & TERIS, a.s., pobočka Brno, Brno, Czech Republic. pp. 77–84. ISBN 80-214-2718-3. A11: 4/2 = 2 b. (20c) VOŘECHOVSKÝ, M. (2004). Stabilita a konvergence numerických metod při simulaci extrémních hodnot rozdělení. In: PPK 2004 Pravděpodobnost porušování konstrukcí. Brno University of Technology, Faculty of Civil Engineering & ústav aplikované mechaniky Brno, s.r.o. & Asociace strojních inţenýrů & TERIS, a.s., pobočka Brno, Brno, Czech Republic, pp. 257–264. ISBN 80-214-2718-3. A11: 4/1 = 4 b. 2005 (24z) MATESOVÁ, D., VOŘECHOVSKÝ, M. (2005). Reduction functions for mechanical/fracture parameters of concrete at elevated temperatures. In: Králik, J. (Ed.), Proc. of the 4th International Conference on New Trends in Statics and Dynamics of Buildings. Faculty of Civil Engineering STU Bratislava and Slovak Society of Mechanics SAS, Bratislava, Slovakia, p. 165, CD Proc. ISBN 80-2272277-4. A11: 4/2 =2 b. (21c) ŘOUTIL, L., FRANTÍK, P., LEHKÝ, D., KERŠNER, Z., NOVÁK, D., VOŘECHOVSKÝ, M., KNĚZEK, J., ZAVŘEL, L. (2005). Proměnlivost lomověmechanických vlastností cementového kompozitu se skleněnými vlákny. In: Lederová, Knězek and Svoboda (Eds.), IX. konference Výzkumného ústavu stavebních hmot: Ekologie a nové stavební hmoty a výrobky. VUSTAH, Telč, Czech Republic, pp. 194–198. ISBN 80-239-4905-5. A11: 4/2 =2 b. (22c) VOŘECHOVSKÝ, M., BAŢANT, Z. P., NOVÁK, D. (2005). Procedure of statistical size effect prediction for crack initiation problems. In: Bílek and Keršner (Eds.), 2nd International symposium Nontraditional cement & concrete. Brno University of Technology, Brno, Czech Republic, pp. 433–438. ISBN 80-214-2853-8. A11: 4/2 =2 b. (23c) VOŘECHOVSKÝ, M., CHUDOBA, R. (2005). Numerical modeling of delayed activation and statistical size effect in multifilament yarns. In: Bílek and Keršner (Eds.), 2nd International symposium Nontraditional cement & concrete. Brno University of Technology, Brno, Czech Republic, pp. 439–449. ISBN 80-214-2853-8.
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A11: 4/2 =2 b. (24c) CHUDOBA, R., KONRAD, M., VOŘECHOVSKÝ, M., BRUCKERMANN, O. (2005). Textile reinforced concrete: correspondence between the local and global performance in uni-axial stress state. In: Bílek and Keršner (Eds.), 2nd International symposium Nontraditional cement & concrete. Brno University of Technology, Brno, Czech Republic, pp. 450–458. ISBN 80-214-2853-8. A11: 4/2 =2 b. (25c) MATESOVÁ, D., LEHKÝ, D., VOŘECHOVSKÝ, M. (2005). Kvazikřehké materiály při vysokých teplotách: numerické modelování. In: Šrůma, V., Šrůmová, Z. (Eds.), Betonářské dny 2005. Czech Concrete Society with ČBS Servis, ltd., Hradec Králové, Czech Republic, pp. 455–460. ISBN 80-903502-2-4. A11: 4/2 =2 b. 2006 (26c) ELIÁŠ, J.,VOŘECHOVSKÝ, M. (2006). SMARTedt: SMoothing by Averaging and Reduction of Testing data. In: Kolektiv (Ed.), Sborník příspěvků konference Modelování v mechanice. VŠB-TU Ostrava, Fakulta stavební, Ostrava, Czech Republic, pp. 53–54 Extended abstract, full length paper on CDROM. ISBN 80-2481035-2. A11: 4/2 =2 b. (27c) VOŘECHOVSKÝ, M., MATESOVÁ, 2006. Interplay of size effects in concrete specimens by computational stochastic fracture mechanics. In: Náprstek, J., Fischer, C. (Eds.), Engineering Mechanics 2006. Institute of Theoretical and Applied Mechanics, Academy of Sciences of the Czech Republic, Prague, Svratka, Czech Republic, pp. 1–15, Extended Abstract: 434–435. ISBN 80-86246-27-2. A11: 4/2 =2 b. (28c) VOŘECHOVSKÝ, M., MATESOVÁ, D. (2006). Modelování vlivu velikosti betonových konstrukcí pomocí nelineární stochastické lomové mechaniky. In: Vejvoda, et al. (Eds.), PPK 2006 Pravděpodobnost porušování konstrukcí. Brno University of Technology, Faculty of Civil Engineering and Faculty of Mechanical Engineering & ústav aplikované mechaniky Brno, s.r.o. & Asociace strojních inţenýrů & TERIS, a.s., pobočka Brno, Brno, Czech Republic, pp. 269–284. ISBN 80-2143251-9. A11: 4/2 =2 b. (29c) VOŘECHOVSKÝ, M. (2006). Hierarchical Subset Latin Hypercube Sampling. In: Vejvoda, et al. (Eds.), PPK 2006 Pravděpodobnost porušování konstrukcí. Brno University of Technology, Faculty of Civil Engineering and Faculty of Mechanical Engineering & ústav aplikované mechaniky Brno, s.r.o. & Asociace strojních inţenýrů & TERIS, a.s., pobočka Brno, Brno, Czech Republic, pp. 285–298. ISBN 80214-3251-9. A11: 4/1 =4 b. (30c) MATESOVÁ, D., VOŘECHOVSKÝ, M. (2006). Reinforcement corrosion in concrete: analytical approach to modelling. In: Vejvoda, et al. (Eds.), PPK 2006 Pravděpodobnost porušování konstrukcí. Brno University of Technology, Faculty of
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
Civil Engineering and Faculty of Mechanical Engineering & Ústav aplikované mechaniky Brno, s.r.o. & Asociace strojních inţenýrů & TERIS, a.s., pobočka Brno, Brno, Czech Republic, pp. 313–320. ISBN 80-214-3251-9. A11: 4/2 =2 b. (31c) VOŘECHOVSKÝ, M. (2006). Statistical, sensitivity and reliability analyses: problem revision and software design. In: Pejchalová, J., Kala, J., Keršner, Z. (Eds.), Směřování kateder/ústavu STM stavebních fakult ČR a SR 2005/2006. Brno University of Technology, Fac. of Civil Engrg., Institute of Struct. Mech. Mikulov, Czech Republic, pp. 81–92. ISBN 80-214-3248-9 A11: 4/1 =4 b. (32c) VOŘECHOVSKÝ, M. (2006). Korekce zatěţovacích drah na netuhých lisech. In: Pejchalová, J., Kala, J., Keršner, Z. (Eds.), Směřování kateder/ústavu STM stavebních fakult ČR a SR 2005/2006. Brno University of Technology, Fac. of Civil Engrg., Institute of Struct. Mech. Mikulov, Czech Republic, pp. 93–97. ISBN 80-2143248-9 A11: 4/1 =4 b. 2007 (33c) MATESOVÁ, D., VOŘECHOVSKÝ, M. (2007). Modeling of reinforcement corrosion in concrete. In: Zolotarev, I. (Ed.), Engineering Mechanics 2007. Institute of Thermomechanics, Academy of Sciences of the Czech Republic, Prague. Svratka, Czech Republic, pp. 1–11 (CD–ROM), Extended Abstract: 179–180. ISBN 978-80-87012-06-2. A11: 4/2 =2 b. (34c) FRANTÍK, P., VOŘECHOVSKÝ, M. (2007). Dynamical model of a flexible tube. In: Zolotarev, I. (Ed.), Engineering Mechanics 2007. Institute of Thermomechanics, Academy of Sciences of the Czech Republic, Prague. Svratka, Czech Republic, pp. 1–8 (CD–ROM), Extended Abstract: 65–66. ISBN 978-80-87012-06-2. A11: 4/2 =2 b. (35c) VOŘECHOVSKÝ, M., ELIÁŠ, J. (2007). Modelování nelinární nelineární mechanické odezvy vláknobetonu v programu Atena. In: 11th International Conference of Research Institute of Building Materials: Ecology and new building materials and products. Telč, Czech Republic, p. in print, ISBN xx-xx-xxxx-x A11: 4/2 =2 b. (36c) JEŘÁBEK, J., CHUDOBA, R., VOŘECHOVSKÝ, M., MOMBARTZ, M.: (2007). Adaptive time-stepping algorithm exploiting the combined effect of softening and spatial variability of material parameters. In: Modelling of Heterogeneous Materials with Application in Construction and Biomedical Engineering. Prague, Czech Republic, p. in print. ISBN xx-xx-xxxx-x. A11: 4/2 =2 b. (37c) ELIÁŠ, J., VOŘECHOVSKÝ, M.: (2007). On modeling experiments with FRC in Atena software. In: Fibre Concrete 2007, 4th International Conference. Prague, Czech Republic, p. in print. ISBN xx-xx-xxxx-x. A11: 4/2 =2 b.
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2) Uznání vědeckou a odbornou komunitou 2.1) Ocenění, soutěže a stipendia
Nebodováno
Cena rektora, Vysoké učení technické v Brně Cena děkana, Vysoké učení technické v Brně Cena za diplomovou práci, Vysoké učení technické v Brně Doktorské stipendium, Hlávkova nadace, Praha Electricité de France, Conference Stipend of Excellence, FraMCoS, France, Paris, 2001 (úhrada vloţného, cesty, stravného, ubutování na École Normale supérieure de Cachan v průběhu konference FRaMCoS, uděleno 10 cen v soutěţi) 04/2002 Cena Josefa Hlávky, Nadace Nadání Josefa, Marie a Zdeňky Hlávkových, Praha 05/2002 Konferenční stipendium, konference v Dundee, Skotsko, Nadace Nadání Josefa, Marie a Zdeňky Hlávkových, Praha 9/2002–6/2003 Preciosa stipendium 09/2002 Ocenění za vynikající článek (9z), 4th International Ph.D. Symposium in Civil Engineering, Munich, Germany 05/2003 M.I.T. Young Researcher Fellowship (award), 2nd M.I.T. Conference, Boston, USA (úhrada vloţného, zpáteční letenky, stravného, ubutování na M.I.T. Boston v průběhu konference) 06/2003 Cerra award – cena společnosti Civil Engineering Risk and Reliability Association (CERRA)), udělena v San Francisco, USA při příleţitosti konání konference ICASP 9, 09/2003–03/2004 Fulbright Doctoral Fellowship, Northwestern University, Evanston, USA (úhrada veškerých výdajů s věedckým pobytem na pracovišti a cesty) 9/2004 Cena rektora, Vysoké učení technické v Brně 2/2005 Trimo Research Award 2005, Trimo corporation, Slovenia 2007 Záznam profesního CV v knize „ Marquis Who’s who in the World 2007―, 24. edice 6/2000 6/2000 6/2000 01/2001 05/2001
2.2) Vyzvané přednášky
Nebodováno
Pozvání k přednáškám na univerzity s úhradou nákladů: Applied stochastic fracture mechanics and size effect, Technická universita v Cáchách (RWTH Aachen), Německo, 2003. (Kód 1l) Determination of material characteristics of multi-filament yarns: Phenomena, Experiments, Models and thein relations. Technická universita v Cáchách (RWTH Aachen), Německo, 2003. (Kód 2l) Impact of scatter of material properties on the yarn performance in TRC. Mezinárodní symposium RILEM on Textile Reinforced Concrete. Technická universita v Cáchách (RWTH Aachen), Německo. 2006. (Kód 34z) Stochastické modelování mechanického chování vláken, svazků vláken a kompozitů vyztuţených vlákny. Přednášky pro doktorandy a zaměstnance TUL: Technická univerzita v Liberci. 2006. (Kód 6l)
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Ing. Miroslav Vořechovský, Ph.D.
Pozvání k přednášce na zahraniční univerzitě bez úhrady nákladů (pracovník přítomen v místě přednášky): Numerické stanovení materiálových charakteristik mnoho-vláknitých svazků. Vysoké učení technické v Brně, Fakulta stavební, Ústav stavební mechaniky, 2003. Kód 3l Material characteristics of composite structure: multi-filament yarns and textile reinforced concrete. Northwestern University, Evanston, USA, 2003. Sponzor: Fulbright commision. Kód (4l) Statistical alternatives of combined size effect on nominal strength for structures failing at crack initiation. Lecture given at the Czech Academy of Sciences - Institute of physics of materials of the ASCR, 2004. Kód (5l) 2.3) Mezinárodní citace Podtrţení jména citujícího autora značí, ţe tento provádí self-citaci. Analogie platí pro citovanou publikaci. Takové citace nejsou započteny do bodového hodnocení. Zjevné selfcitace (vlastní články uchazeče) nejsou vůbec uváděny. KUČEROVÁ, A., LEPŠ, M., ZEMAN, J.: Back analysis of microplane model parameters using soft computing methods, CAMES: Computer Assisted Mechanics and Engineering Sciences, in press, 2007 3r; A6: 1x3 = 3 b. BAŢANT, Z.P, PANG, S-D.: Activation energy based extreme value statistics and size effect in brittle and quasibrittle fracture, Journal of the Mechanics and Physics of Solids, Volume 55, Issue 1 , January 2007, pp 91-131, doi:10.1016/j.jmps.2006.05.007 4j; poděkování Self-citace = 0 b. ŠEJNOHA, M.; ŠEJNOHA, J.; KALOUSKOVÁ, M.; ZEMAN, J.: Stochastic analysis of failure of earth structures, Probabilistic Engineering Mechanics, Volume 22, Issue 2, April 2007, Pages 206-218, doi:10.1016/j.probengmech.2006.11.003, ISSN 0266-8920. 11z A6: 1x3 = 3b. NOVÁK, D.; LEHKÝ, D.: ANN inverse analysis based on stochastic small-sample training set simulation, Engineering Applications of Artificial Inteligence (Elsevier), Volume 19, Issue 7, October 2006, pp 731-740 11z; 12z; 23z; 25z; 5r; 6r; Self-citace = 0 b. DILTHEY, U.; SCHLESER, M.; HANISCH, V.; GRIES, T.: Garnzugprüfung polymergetränkter Textilien für die Bewehrung von Beton (Yarn tensile test of polymer-impregnated textiles for the reinforcement of concrete), Technische Textilien 49 (2006), H. 1, S. 48-50 (Technical Textiles 49 (2006), H. 1, S. E41-E43) 1j; 2j A6: 2x3 = 6 b. BANHOLZER, B.; BROCKMANN, T.; BRAMESHUBER, W. Material and boning characteristics and modelling of textile reinforced concrete (TRC) elements. Materials and Structures. 2006, vol. 39, is. 8, s. 749-763. ISSN 1359-5997. Dostupný z WWW: DOI 10.1617/s11527-006-9140-x>. 1j; 2j A6: 2x3 = 6 b.
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Ing. Miroslav Vořechovský, Ph.D.
STRAUSS, A.; MORDINI, A.; BERGMEISTER, K.: Nonlinear finite element analysis of reinforced concrete corbels at both deterministic and probabilistic levels, Computers and Concrete. Volume 3, Number 2, April/June 2006, , ISSN: 1598-8198 11z; 12z A6: 2x3 = 6 b. CHUDOBA, R.; GRAF, W.; MESKOURIS, K.; ZASTRAU, B.: Numerische Modellierung von textilbewehrtem Beton, Beton- und Stahlbetonbau, Volume 99, Issue 6 , Pages 460 – 465, 2004, doi:10.1002/best.200490117 1j Self-citace = 0 b. STRAUSS, A., KALA, Z., KONRAD BERGMEISTER, K., HOFFMANN, S., NOVÁK, D. 2006 Technologische Eigenschaften von Stählen im europäischen Vergleich, Stahlbau 75 (1), DOI: 10.1002/stab.200610007 3r; 11z Self-citace = 0 b. BERGMEISTER, K.; CURBACH, M.; STRAUSS, A.; PROSKE, D.; NORDHUES, H.W.: (2006): Sicherheit und Gefährdungspotenziale im Industrie- und Gewerbebau.. In: Bergmeister Konrad, Wörner Johann-Dietrich, Kniha BetonKalender (Turmbauwerke und Industriebauten), 2, p. 291; Ernst & Sohn Verlag, Berlin; ISBN 3-433-01672-0. pages 289354, 11z A14:1x1 = 1 b. BAŢANT, Z.P.; YAVARI, A. 2005. Is the cause of size effect on structural strength fractal or energetic–statistical?, Engineering Fracture Mechanics, p. 1–31 14z Self-citace = 0 b. STRAUSS, A.; BERGMEISTER, K.; NOVÁK, D.; LEHKÝ, D. Stochastische Parameteridentifikation bei Konstruktionsbeton für die Betonerhaltung. Beton und Stahlbetonbau, Vol. 99, No. 12, Vienna, Austria, 2004, p.967-974, ISSN 0005-9900. 11z Self-citace = 0 b. BAŢANT, Z.P.: Scaling theory for quasibrittle structural failure (Inaugural Article), Proc Natl Acad Sci U S A. 004, The National Academy of Sciences, 2004 September 14; 101(37): 13400–13407. 2004, doi: 10.1073/pnas.0404096101. 14z Self-citace = 0 b. BAŢANT, Z.P.; PANG, S-D. Activation Energy Based Extreme Value Statistics and Size Effect in Brittle and Quasibrittle Fracture, Journal of the Mechanics and Physics of Solids, In print. 4j Self-citace = 0 b. SUDA, J., STRAUSS, A., RUDOLF-MIKLAU, F. AND HUEBL, H. (2007). Safety Assessment of Barrier Structures. Journal of Structure and Infrastructure Engineering; Maintenance, Life-Cycle, Design and Performance. (Review Paper) ISSN: 1744-8980. 11z; 5r A6: 2x3 = 6 b. SANTA, U.; BERGMEISTER, K.; STRAUSS, A. 2004.Bauwerksüberwachung der Autobahnbrücke Gossensaß. Beton- und Stahlbetonbau, Volume 99, Issue 12, pp 975 984, http://dx.doi.org/10.1002/best.200490283 2r A6: 1x3 = 3 b.
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Ing. Miroslav Vořechovský, Ph.D.
BERGMEISTER, K.; STRAUSS, A.; SANTA, U. (2005). Concepts for health judgement of structures. In: Bergmeister, K., Strauss, A., Rickenmann, D. (Eds.), Proc. of the 3rd Probabilistic Workshop: Technical Systems + Natural Hazards, also Schriftenreihe des Departments Nr. 7 - November 2005. Universität für Bodenkultur Wien, Department für Bautechnik + Naturgefahren, Vienna, Austria, pp. 41–50, ISSN 1811-8747. 11z; 12z; 21z A14: 3x1 = 3 b. PUKL, R.; BERGMEISTER, K. (2005). Safety and reliability assessment of concrete structures and practical applications. In: Bergmeister, K., Strauss, A., Rickenmann, D. (Eds.), Proc. of the 3rd Probabilistic Workshop: Technical Systems + Natural Hazards, also Schriftenreihe des Departments Nr. 7 - November 2005. Universität für Bodenkultur Wien, Department für Bautechnik + Naturgefahren, Vienna, Austria, pp. 61–69, ISSN 18118747. 11z; 12z; 21z A14: 2x1 = 2 b. STRAUSS, A.; BERGMEISTER, K; SANTA, U.; PUKL, R; CERVENKA, V. ; NOVÁK, D. (2003). Non Destructive Reliability Analysis of Concrete Structures Numerical concepts and material models for existing concrete structures. In: International Symposium (NDT-CE 2003) Non-Destructive Testing in Civil Engineering 2003. 7z; 11z; 12z Self-citace = 0 b. KONRAD, M.; CHUDOBA, R.; MESKOURIS, K.; MOMBARTZ, M.: Numerical Simulation of Yarn and Bond Behavior at Micro- and Meso-Level. In proceedings: Curbach (Ed.) 2nd Colloquium on Textile Reinforced Structures (CTRS2).Dresden: Technische Universität Dresden,, Pages 399-410, 2003 1j Self-citace = 0 b. CHUDOBA, R.; KONRAD, M.; MOMBARTZ, M.; HEGGER, J.: Numerische Modellierung des Verbundverhaltens von textilbewehrtem Beton auf der Mikro-, Mesound Makroebene. Werkstoffwoche 2004, München, 21.-23.09.2004. 1j Self-citace = 0 b. KONRAD, M.; CHUDOBA, R.; MESKOURIS, K.: Numerische Modellierung des Verbundverhaltens von textilbewehrtem Beton. In proceedings: Forschungskolloquium Baustatik und Baupraxis, 2003 1j Self-citace = 0 b. GRIES, TH.; ROYE, A; CHUDOBA, R.; KONRAD, M.; PEIFFER, F.: .Prüftechniken an Rovings, Interaktionseffekte und Auswertung im Tagungsband: Dresdner Textiltagung 2004, 2004. 1j Self-citace = 0 b. CHUDOBA, R.; MÖLLER, B.; MESKOURIS, K.; ZASTRAU, B.; GRAF, W.; LEPENIES, I.: Numerical Modeling of Textile Reinforced Concrete im Tagungsband: ACI Fall Convention 2005, New Orleans, 6-10.10. (2005) 1j; 2j Self-citace = 0 b. HANISCH, V.; GRIES, T.; CHUDOBA, R.: Tensile tests on multifilament yarns - results and interactions In: Hamlin, P.; Bigaud, D.; Ferrier, E.; Jacquelin, E. (Eds.): Third Interna-
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Ing. Miroslav Vořechovský, Ph.D.
tional Conference Composites in Construction ; CCC 2005, Lyon/F 11.-13.07.2005. Lyon: Université Lyon I, Laboratoire Mécanique Matériaux et Structures, 2005. Tome 2, S. 1219-1225, Paper: CCC2005_084.PDF 1j; 2j Self-citace = 0 b. HEGGER, J.; SHERIF, A.; BRUCKERMANN, O.; KONRAD, M.: SP-224-3: Textile Reinforced Concrete: Investigations at Different Levels, ACI SPECIAL PUBLICATIONS, „Thin Reinforced Cement-Based Products and Construction Systems―, 2004, NUMB 224, pages 33-44 Publisher: ACI, ISSN 0065-7891 (ACI Spring Convention, Vancouver, 2003) 1j; 2j A6: 1x3 = 3 b. KONRAD, M.; CHUDOBA, R.: The Influence of Disorder in Multifilament Yarns on the Bond Performance in Textile Reinforced Concrete. Acta Polytechnica, Band 44, No. 5-6, pp. 186-193, 2004 1j; 2j Self-citace = 0 b. BAŢANT, Z.P. Rozměrový efekt (size effect), jeho podíl na případech katastrofického zhroucení konstrukcí a důsledky pro návrhové normy, časopis Beton 2/2006, str 22-49 4j; 13z Self-citace = 0 b. BAŢANT, Z.P. Rozměrový efekt, jeho podíl na případech katastrofického zhroucení konstrukcí a důsledky pro návrhové normy, In: 12. Betonářské dny, sekce ―vyzvané přednášky‖, 2005, str 17-36 4j; 5j Self-citace = 0 b. KONRAD, M; CHUDOBA, R; BUTENWEG C; BRUCKERMANN O.: Textile Reinforced Concrete Part II: Multi-Level Modeling Concept. Internationalen Kolloquium über Anwendungen der Informatik und Matematik in Architektur und Bauwesen (International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering), June 10 – 12 2003, ISSN 1611-4086, 1j; 2j Self-citace = 0 b. STRAUSS, A. (2003): Stochastische Modellierung und Zuverlässigkeit von Betonkonstruktionen. Dissertation (PhD thesis) am Institut für Konstruktiven Ingenieurbau, BOKU, 295; Wien 3r; 12z A14: 2x1 = 2 b. STRAUSS, A., LEHKÝ, D., NOVÁK, D. Identification and Monitoring of Concrete Structures Based on Neural Network. MOEL Report, Vienna, Austria, 2004, 66 stran. 3r; 11z Self-citace = 0 b. STRAUSS, A., BERGMEISTER, K., LEHKÝ, D., NOVÁK, D. 2006. Inverse statistical nonlinear FEM analysis of concrete structures. In: Meschke, de Borst, Mang, Bičanič (Eds.), EURO-C 2006 Computational Modelling of Concrete Structures. Taylor & Francis Group, London, Mayrhofen, Austria, pp. 897–904. 11z; 25z; 5r; 6r Self-citace = 0 b. MOMBARTZ, M.; HEGGER, J.; CHUDOBA, R.; PEIFFER, F. 2006. Simulation of textile reinfeorced concrete with 2D crack and discrete crack bridges representation. In:
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
Meschke, de Borst, Mang, Bičanič (Eds.), EURO-C 2006 Computational Modelling of Concrete Structures. Taylor & Francis Group, London, Mayrhofen, Austria, pp. 167–172. 1j; 8j; 28z Self-citace = 0 b. STRAUSS, A., LEHKÝ, D., NOVÁK, D., BERGMEISTER, K., SANTA, U. Probabilistic response identification and monitoring of concrete structures. 3rd European Conference on Structural Conctrol, 3ECSC, Vídeň, Rakousko, 2004. 11z Self-citace = 0 b. BAŢANT, ZP AND SZE-DAI PANG, S-D. Activation Energy Based Extreme Value Statistics and Size Effect in Brittle and Quasibrittle Fracture, Report No. 05-07/C441a at Department of Civil and Environmental Engineering McCormick School of Engineering and Applied Science Northwestern University Evanston, Illinois 60208, USA November 2005 4j Self-citace = 0 b. BANHOLZER, B.: Bond behaviour of a multi-filament yarn embedded in a cementitious Matrix. Dissertation (PhD thesis) Von der Fakultät für Bauingenieurwesen der RheinischWestfälischen Technischen Hochschule Aachen , 2004 1j; 2j A14: 2x1 = 2 b. JESSE, F.: Tragverhalten von Filamentgarnen in zementgebundener Matrix, Dissertation (PhD thesis) Von der Fakultät Bauingenieurwesen der Technischen Universität Dresden, 2004 1j; 2j A14: 2x1 = 2 b. BRUCKERMANN, O.: Zur Modellierung Zugtragverhaltens von Textilbewehrtem Beton, Dissertation (PhD thesis) Von der Fakultät für Bauingenieurwesen der RWTH Aachen, 2006 1j; 2j A14: 2x1 = 2 b. JEŘÁBEK, J.: Stochastic simulation of textile-reinforced concrete using the chain of interacting crack bridges Diplomarbeit, CTU Prag / RWTH Aachen, 2005 1j; 2j A14: 2x1 = 2 b. ZAKRZEWSKI, P.: Enriched Finite Element Approximation for Multi-Cracking and Debonding in Textile Reinforced Concrete, Master Thesis, Matr.Nr. 268934, Faculty of Civil Engineering, RWTH Aachen, 2006 1j; 2j; 8j; 20z A14: 4x1 = 4 b. HÄUßLER-COMBE, U.; HARTIG, J.: Uniaxial Structural behaviour of TRC – a ondimensional approach consdering transverse directionby segmentation. In: ICTRC 2006 1st International RILEM Conference on Textile Reinforced Concrete, 2006, 203-212, ISBN 2912143-97-7. 1j; A14: 1x1 = 1b. KONRAD, M.; CHUDOBA, R.: Numerical evaluation of damage parameters for textile reinforced concrete under cycling loading. In: ICTRC 2006 1st International RILEM Conference on Textile Reinforced Concrete, 2006, 213-222, ISBN 2-912143-97-7. 1j; 2j; Self-citace = 0 b.
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Ing. Miroslav Vořechovský, Ph.D.
MOMBARTZ, M.; PEIFFER, R.; CHUDOBA, R.; MESKOURIS, K.: Simulation evolving discontinuities in TRC using the extended finite element method. In: ICTRC 2006 1st International RILEM Conference on Textile Reinforced Concrete, 2006, 243-252, ISBN 2-912143-97-7. 1j; 8j; Self-citace = 0 b. GRIES, T.; ROYE, A.; OFFERMANN, T.; PELED, A.: Textiles. In: Brameshuber (Ed.) State-of-the-Art Report of RILEM Technical Committee 201-TRC: Textile Reinforced Concrete. RILEM Report 36. 2006, pp. 11—27. ISBN 2-912143-99-3. 1j; A6: 1x3 = 3 b. REINHARDT, H.-W.; BENTUR, A.; BRAMESHUBER, W.; CURBACH, M.; JESSE, F.; MOBASHER, B.; PELED, A.; SCHORN, H.: Composite materials. In: Brameshuber (Ed.) State-of-the-Art Report of RILEM Technical Committee 201-TRC: Textile Reinforced Concrete. RILEM Report 36. 2006, pp. 83—131. ISBN 2-912143-99-3. 8j; 1j; 2j; A6: 3x3 = 9 b. HEGGER, J.; BENTUR, A.; CURBACH, M.; JESSE, F.; MOBASHER, B.; PELED, A.; WASTIELS, J.: Mechanical behaviour of textile reinforced concrete. In: Brameshuber (Ed.) State-of-the-Art Report of RILEM Technical Committee 201-TRC: Textile Reinforced Concrete. RILEM Report 36. 2006, pp. 133—186. ISBN 2-912143-99-3. 1j; 2j; A6: 2x3 = 6 b. Z. SADOVSKÝ: Probability based design codes in Slovakia – Historical development and new challenges, Workshop on Reliability Based Code Calibration, Swiss Federal Institute of Technology, ETH Zurich, Switzerland, March 21-22, 2002. 2z A14: 1x1 = 1b. BRODŇAN, M; ŠLOPKOVÁ, K.: Simulation of corrosion of reinforced concrete beams. In: Quality and Reliability in Building Industry, proc. Of the IV. International Scientific Conference, Levoča, Slovak Republic. Technical University Košice, October 2006. pp. 5356. ISBN 80-8073-594-8. 1r A14: 1x1 = 1b. 2.5) Domácí citace KALA, Z., Verification of the Partial Reliability Factors on a Case of a Frame With Respecting Random Imperfections, International Colloquium, In: Proc. International Conference on Metal Structures, Miskolc (Hungary), Edited by K. Jarmai & J. Farkas, Proceedings pp.19-22, 2003, Millpress Science Publishers, Rotterdam, ISBN 90 77017 75 5. 2z Self-citace = 0 b. KERŠNER, Z., NÁHLÍK, L., KNÉSL, Z. Concrete as a two-phase material: statistical/sensitivity modelling of failure. In Book of extended abstracts p. 154-155, full text – CD ROM ENGINEERING MECHANICS, Svratka, 2003. ISBN 80-86246-18-3. 2r; 14c; 9z A14: 2x1 = 2b. NOVÁK, D., LEHKÝ, D. a KERŠNER, Z. Lomově-mechanické parametry vysokohodnotného betonu: experiment, modelování a identifikace (Fracture/mechanical parameters of High Performance Concrete: test, modelling and identification), In Sborník
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
sympozia Fibre Concrete & High Performance Concrete FC & HPC 2003, Malenovice, 2003, s. 67-74. ISBN 80-86604-08-X. 2r Self-citace = 0 b. NOVÁK, D.; KERŠNER, Z. Statistická a spolehlivostní analýza výpočtově náročných problémů simulačními metodami typu Monte Carlo (Statistical and reliability analysis of time-consuming solution of problems by Monte Carlo type simulation methods), In Sborník semináře Uplatnění pravděpodobnostních metod při navrhování konstrukcí, Praha, 2003, s. 155-175. ISBN 80-01-02826-7. 2r; 1z; 7c Self-citace = 0 b. LEHKÝ, D. Identifikace materiálových parametrů konstitutivních modelů pro beton (Identification of material parameters of constitutive models for concrete). 5. Odborná konference doktorského studia, VUT v Brně, Fakulta stavební, Brno, 2003, ISBN 80-7204-2653, (CD-ROM). 7z A14: 1x1 = 1b. LEHKÝ, D., NOVÁK, D. Identifikace parametrů materiálových modelů (Identification of material models parameters). Konf. Problémy lomové mechaniky III., Brno, Česká Republika, 2003, pp. 48-54, ISBN 80-214-2392-7. 2r Self-citace = 0 b. ŘOUTIL, L., LEHKÝ, D. Tříbodový ohyb trámce se zářezem – statistická a citlivostní analýza ATENA/FREET (Three-point bending of notched beam – statistical and sensitivity analysis). Konf. Problémy lomové mechaniky III., Brno, Česká Republika, 2003, pp. 6167, ISBN 80-214-2392-7. 2r Self-citace = 0 b. MATESOVÁ, D., VESELÝ, V. Prestressed sleeper – statistical/sensitivity analysis of 4 PB test. In Proc. of Modelování v mechanice. Ostrava: FAST VŠB-TUO, 2003, p. 107111. ISBN 80-248-0253-8. 11z; 9z A14: 2x1 = 2b. BAŢANT, Z.P., NOVÁK, D. Keynote paper: Stochastic Models for Deformation and Failure of Quasibrittle Structures: Recent Advances and New Directions. Proceedings of the Euro-C Conference, St. Johann im Pongau, 17-20 March 2003, pp. 583-598, Swets & Zeitlinger, Lisse, ISBN 90-5809-936-3 11z; 1z Self-citace = 0 b. PUKL, R., ČERVENKA, V., STRAUSS, A., BERGMEISTER, K., NOVÁK, D. An Advanced Engineering Software for Probabilistic-Based Assessment of Concrete Structure Using Nonlinear Fracture Mechanics, ICASP 9, 9th International Conference on Applications of Statistics and Probability in Civil Engineering, San Francisco, USA, July 6-9, pp. 1165-1171,Millpress, Rotterdam, ISBN 90-5966-004-8 11z; 12z Self-citace = 0 b. MENŠÍK, M. 2004. Přibliţné řešení stochastické okrajové úlohy tlakového proudění pozdemní vody, In: Cásková et al. (Eds), 4. Vodohospodářská konference 2004 s mezinárodní účastí, Fakulta stavební VUT v Brně, ISBN: 80-86433-26-9, pp. 288—295 10z A14: 1x1 = 1b.
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
PUKL, R., NOVÁK, D., BERGMEISTER, K. Reliability Assessment of Concrete Structures. Proceedings of the Euro-C Conference, St. Johann im Pongau, 17-20 March 2003, pp. 793-803, Swets & Zeitlinger, Lisse, ISBN 90-5809-936-3 7z; 11z; 9z; 12z Self-citace = 0 b. PUKL, R., JANSTA, M., ČERVENKA, V. NOVÁK, D. ATENA-SARA - Integrovaný programový systém pro analýzu stavebních konstrukcí s vyuţitím pravděpodobnostních metod v nelineární metodě konečných prvků. Seminář Uplatnění pravděpodobnostních metod při navrhování konstrukcí, ČVUT v Praze, Kloknerův ústav, 1.-2. října 2003, s. 177-187, ISBN 80-01-02826-7. 2r Self-citace = 0 b. KALA, Z., Reliability of Steel Structures in Compliance with the Principles of the EN 1990-Theoretical and Experimental Studies, Part 2, Journal of Engineering Mechanics (Inţenýrská mechanika), Brno: VUT, 2004, 11/ 2004, No.2, pp.115–124, ISSN 1210 2717. 2z Self-citace = 0 b. LEHKÝ, D. Inverzní stochastická analýza betonových konstrukcí, Doktorská dizertační práce, Ústav stavební mechaniky, Fakulta Stavební, Vysoké učení technické v Brně. 2006. 9z; 11z; 12z; 3r A14: 3x1 = 3b. STIBOR, M. Lomové parametry kvazikřehkých materiálů a jejich určování, Doktorská dizertační práce, Ústav stavební mechaniky, Fakulta Stavební, Vysoké učení technické v Brně. 20046. 11z; 5z; 4z A14: 2x1 = 2b. KALA, Z., KALA, J., ŠKALOUD, M., TEPLÝ, B., MELCHER, J., NOVÁK, D. Sensitivity Analysis of Engineering Structures, In CD Proc. of the European Congress on Computational Methods in Applied Sciences and Engineering - ECCOMAS 2004, Jyväskylä (Finland), 2004, ISBN 951-39-1868-8. 11z Self-citace = 0 b. ŘOUTIL, L., KERŠNER., Z. Pravděpodobnostní analýza šířky trhlin předpjatého praţce při kontrolní zkoušce. Sborník konference Pravděpodobnost a porušování konstrukcí 2004, Brno, 2004, 307-312, ISBN 80-214-2718-3. 2r; 11z A14: 1x1 = 1b. KERŠNER, Z., ROVNANÍKOVÁ, P., TEPLÝ, B., NOVÁK, D. Design for durability: An interactive tool for RC structures. Sborník mezinárodní konference LC 2004 – Life Cycle Assessment, Behaviour and Properties of Concrete and Concrete Structures, Brno, 2004, 172-182, ISBN 80-214-2370-6. 3r; 11z Self-citace = 0 b. NOVÁK, D., LEHKÝ, D. Neural network based identification of material model parameters to capture experimental load-deflection curve. Acta Polytechnica, Praha, Česká republika, 2004. 3r; 11z Self-citace = 0 b.
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
LEHKÝ, D., NOVÁK. Identification of material model parameters using stochastic training of neural network. 5th PhD. Symposium in Civil Engineering, Delft, Netherlands, 2004, pp. 535-542, ISBN 9058096777. 3r; 11z Self-citace = 0 b. LEHKÝ, D., NOVÁK, D. K posouzení účinnosti simulačních metod typu Monte Carlo. V. ročník celostátní vzdělávací akce Spolehlivost konstrukcí: Metodika – aplikace – poruchy – havárie. Ostrava, Česká Republika, 2004, pp. 55-60, ISBN 80-248-0573-1. 3r, 11z Self-citace = 0 b. LEHKÝ, D., NOVÁK, D. Identifikace statistických parametrů materiálových modelů. Konf. PPK 2004 – Pravděpodobnost porušování konstrukcí, Brno, Česká republika, 2004, pp. 63-68, ISBN 80-214-2718-3. 11z Self-citace = 0 b. BERGMEISTER, K., NOVÁK, D., PUKL, R. SARA: An advanced engineering tool for reliability assessment of concrete structures. Progress in Structural Engineering, Mechanics and Computation, Zingoni (ed.), Proc. of second int. conf., Cape Town, South Africa, Taylor&Francis Group, London, 2004, p.899-904, ISBN 90-5809-568-1. 11z; 12z Self-citace = 0 b. NOVÁK, D. Simulační metody a analýza výpočtově náročných úloh mechaniky kontinua. Konf. PPK 2004 – Pravděpodobnost porušování konstrukcí, Brno, Česká republika, 2004, s.25-34, ISBN 80-214-2718-3. 3r; 1z; 11z; 7c; 12z; 13z Self-citace = 0 b. ŠIGUT, O., NOVÁK, D. Pravděpodobnostní analýza ţelezobetonových nosníků porušujících se smykem s ověřením spolehlivosti návrhových vztahů. Konf. PPK 2004 – Pravděpodobnost porušování konstrukcí, Brno, Česká republika, 2004, s.293-298, ISBN 80-214-2718-3. 3r Self-citace = 0 b. MATERNA. A.; BROŢOVSKÝ, J. (2006). Příspěvek k analýze stavebních konstrukcí s uváţením náhodného charakteru vybraných vstupních veličin. In: Pejchalová, J., Kala, J., Keršner, Z. (Eds.), Směřování kateder/ústavu STM stavebních fakult ČR a SR 2005/2006. Brno University of Technology, Fac. of Civil Engrg., Institute of Struct. Mech. Mikulov, Czech Republic, pp. 81–92. ISBN 80-214-3248-9 12c A14: 1x1 = 1b. ELIÁŠ, J. Chování křehkých vláken v kompozitech s křehkou a kvazikřehkou matricí. Diplomová práce. Ústav stavební mechaniky FAST VUT v Brně (2006). 1j;26c;11z;1t;2t;26z;12z;18z A14: 7x1 = 7b. KALA, Z. 2002. The influence of alternative methods in EN 1990 on reliability of a steel frame - a FEM Importance Sampling solution. Proceedings of the international conference "Reliability and diagnostics of transport structures and means" University of Pardubice, Czech Republic, 26-27 September 2002, ISBN 80-7194-464-5. 2z Self-citace = 0 b.
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Ing. Miroslav Vořechovský, Ph.D.
VESELÝ, V.; KERŠNER, Z.; MOSLER, T.; BÍLEK, V. 2002. Prestressed Railway Sleepers: Experiments and Statistical Modelling. Proceedings of the international conference "Reliability and diagnostics of transport structures and means" University of Pardubice, Czech Republic, 26-27 September 2002, ISBN 80-7194-464-5 14c; 9z A14: 2x1 = 2b. PUKL, R., ČERVENKA, V., NOVÁK, D. K pravděpodobnostní nelineární analýze stavebních konstrukcí. Konf. PPK 2004 – Pravděpodobnost porušování konstrukcí, Brno, Česká republika, 2004, s.55-62, ISBN 80-214-2718-3. 3r; 2t; 14z Self-citace = 0 b. KALA, Z., STRAUSS, A., MELCHER, J., NOVÁK, D., FAJKUS, M., ROZLÍVKA, L. Comparison of Material Characteristics of Austrian and Czech Structural Steels, International Journal of Materials & Structural Reliability, Vol.3 No.1, 2005, p.43-51, ISSN 16856368. 3r; 11z Self-citace = 0 b. ŘOUTIL, L., KERŠNER, Z., VESELÝ, V. Modelování chování předpjatého praţce při kontrolní zkoušce: vliv proměnlivosti předpětí (Numerical modelling of test of prestressed railway sleeper: variability of prestressed steel properties). Sborník Modelování v mechanice 2005, Ostrava, 2005, p. 217-220, ISBN 80-248-0776-9. 2r Self-citace = 0 b. KERŠNER, Z., NOVÁK, D., ŘOUTIL, L., LEHKÝ, D., KNĚZEK, J. Fracture-mechanical parameters of fibre-reinforced cement-based composite for statistical modelling (Lomověmechanické parametry pro statické modelování cementového kompozitu s vlákny). Sborník mezinárodního sympózia Non-Traditional Cements and Concrete II, Brno, 2005, p. 548-553, ISBN 80-214-2853-8. 2r; 21c Self-citace = 0 b. LEHKÝ, D., ŘOUTIL, L., KERŠNER, Z., NOVÁK, D. KNĚZEK, J. Inverzní analýza sklovláknobetonu pro identifikaci lomově.mechanických parametrů (Inverse analysis of glassfibre reinforced for the identification of fracture-mechanical parameters). Sborník konference FC 2005 – Vláknobetony, Malenovice, 2005, p. 227-232, ISBN 80-248-08528. 21c Self-citace = 0 b. ČERVENKA, V., NOVÁK, D., LEHKÝ, D., PUKL, R. Identification of shear wall failure mode. 11th International Conference on Fracture – ICF XI, Turin, Italy, 2005, p. 209 and Proceedings of CD, p. 6 (rozšířený článek). 11z Self-citace = 0 b. NOVÁK, D., LEHKÝ. D. Inverse analysis based on small-sample stochastic training of neural network. 9th International Conference on Engineering Applications of Neural Networks – EANN2005, Lille, France, 2005, p. 155-162. 2r; 11z; 12z Self-citace = 0 b. LEHKÝ, D., NOVÁK, D. Probabilistic inverse analysis: random material parameters of reinforced concrete frame. 9th International Conference on Engineering Applications of Neural Networks – EANN2005, Lille, France, 2005, p. 147-154.
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Návrh na zahájení habilitačního řízení – P12
2r; 11z
Ing. Miroslav Vořechovský, Ph.D.
Self-citace = 0 b.
NOVÁK, D., LEHKÝ, D. Inverse FEM analysis I: Stochastic training of neural network. Inženýrská mechanika 2005, Svratka, Česká Republika, 2005, p. 233-234, ISBN 8085918-93-5 and Proceedings of CD, p. 12 (rozšířený článek). 2r; 11z; 12z Self-citace = 0 b. LEHKÝ, D., NOVÁK, D. Inverse FEM analysis II: Random parameters identification of reinforced concrete frame. Inženýrská mechanika 2005, Svratka, Česká Republika, 2005, p. 197-198, ISBN 80-85918-93-5 and Proceedings of CD, p. 11 (rozšířený článek). 2r; 11z Self-citace = 0 b. NOVÁK, D., TEPLÝ, B., PERNICA, F., PUKL, R.: Probabilistic assessment of existing deteriorating concrete structures. Reliability, Safety and Diagnostics of Transport Structures and Means 2005, Pardubice 2005, s. 257-264, ISBN 80-7194-769-5. 11z; 21z; 12z Self-citace = 0 b. NOVÁK, D., PUKL, R., ŠIGUT, O. Smykové porušení ţelezobetonových nosníků: virtuální modelování spolehlivosti návrhových vztahů. Sborník z konference 12. Betonářské dny 2005, Hradec Králové, 2005, p. 225-230, ISBN 80-903502-2-4. 21z Self-citace = 0 b. NOVÁK, D. Spolehlivostní přístupy a nelineární MKP modely pro zesilování betonových konstrukcí. Seminář Zesilování betonových a zděných konstrukcí, Brno, 2005, p. 66-69, ISBN 80-903501-6-X. 2r; 11z; 7c Self-citace = 0 b. MATESOVÁ, D., CHROMÁ, M., ROVNANÍK, P., TEPLÝ, B.: Tools for assessment of durability of concrete structures, In: PPK Pravděpodobnost porušování konstrukcí Vejvoda et al (eds), Vysoké učení technické v Brně, ISBN: 80-214-3251-9, 2006 30c; 2r; 5r; 6r; 12z A14: 3x1 = 3b. BUČEK, J., RUSINA, R.: Statitická analýza odezvy podloţí na přitíţení, In: PPK Pravděpodobnost porušování konstrukcí Vejvoda et al (eds), Vysoké učení technické v Brně, ISBN: 80-214-3251-9, 2006 5r; A14: 1x1 = 1b. MYNARZ, M., KREJSA, M.: Moţnosti stochastického nelineárního modelování ţelezobetonové konstrukce, In: PPK Pravděpodobnost porušování konstrukcí Vejvoda et al (eds), Vysoké učení technické v Brně, ISBN: 80-214-3251-9, 2006 1r; 2r; A14: 2x1 = 2b. JANAS, P., KREJSA, M., KREJSA, V.: Softwarové prostředky pro aplikaci PDPV, In: PPK Pravděpodobnost porušování konstrukcí Vejvoda et al (eds), Vysoké učení technické v Brně, ISBN: 80-214-3251-9, 2006 11z A14: 1x1 = 1b. JANAS, P., KREJSA, M.: Výpočet pravděpodobnosti poruchy přímým determinovaným pravděpodobnostním výpočtem. In: VI. Konference Spolehlivost kontrukcí, Téma: Od
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
deterministického k pravděpodobnostnímu pojetí inţenýrského posudku spolehlivosti konstrukcí 6.4.2005, Dům techniky Ostrava 11z A14: 1x1 = 1b. NOVÁK, D. Spolehlivostní software: současné trendy, In: PPK Pravděpodobnost porušování konstrukcí. In: Vejvoda et al (eds), Vysoké učení technické v Brně, ISBN: 80214-3251-9, 2006 11z; 5r; 6r; 25z; 21z Self-citace = 0 b. ŘOUTIL, L., KERŠNER, Z., VESELÝ, V.: Vliv degradace na spolehlivost fasádních panelů ze sklovláknového kompozitu, In: PPK Pravděpodobnost porušování konstrukcí Vejvoda et al (eds), Vysoké učení technické v Brně, ISBN: 80-214-3251-9, 2006 5r; 6r A14: 2x1 = 2b. KERŠNER, Z.; Křehkost a lomová mechanika cementových kompozitů. Habilitační práce, Fakulta stavební, Vysoké Učení Technické v Brně, 2005. 2r; 5r;6r; 2t; 9z; 11z; 13z; 14z; 15z; 22z;23z;14c A14: 11x1 = 11b. RAMÍK, Z.; LEHKÝ, D.; VEJVODA, S.; NOVÁK, D. 2006. Inverse analysis of loading of the walking undercarriage frame of giant machine ZP 10000, In: Engineering Mechanics 2006, Svratka, Czech Republic, 2006 5r; 6r Self-citace = 0 b. KUČEROVÁ, A. Material parameters identification for damage model with cracks. In: 3rd PhD Workshop Brno-Prague-Weimar, also ISM-Bericht 1/2006 Bauhaus-Univeristät Weimar. Weimar, Germany, pp. 13—14. 5r; 6r A14: 2x1 = 2b. KALA, Z: Reliability of steel structures in compliance with the principles of the EN 1990theoretical and experimental studies, part II, Inţenýrská mechanika (Engineering mechanics), No 2, Volume 1, year 2004 2z Self-citace = 0 b. A. KUČEROVÁ, A., LEPŠ, M., ZEMAN, J. 2006, Inverse analysis using soft-computing methods: A review. In: Engineering Mechanics 2006, Svratka, 11z A14: 1x1 = 1b. NOVÁK, D. 2002. Stochastic modelling of failure and size effect of concrete structures, In: Barry H. V. Topping and Zdenek Bittnar (Editors) Sixth International Conference on Computational Structures Technology, held in Prague, Czech Republic, in September 2002. Booktitle: Computational structures technology, pp. 93 - 122, ISBN:1-874672-16-4, Civil-Comp press Edinburgh, UK, 7z; 1z; 6z Self-citace = 0 b. MENČÍK, J. S Simulační posuzování spolehlivosti při korelovaných veličinách, 2003, IV. ročník celostátní konference Spolehlivost konstrukcí, Téma: Posudek - poruchy - havárie 23.aţ 24.4.2003 Dům techniky Ostrava ISBN 80-02-01551-7 151 6z; 12c A14: 2x1 = 2b.
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Ing. Miroslav Vořechovský, Ph.D.
KONEČNÝ, P., Vyuţití metody konečných prvků při posudku spolehlivosti metodou SBRA, Teze disertacní práce, VŠB-Technická univerzita Ostrava, Fakulta stavební, Ostrava, kveten 2005 12c; 11z A14: 2x1 = 2b. KOHOUTKOVÁ, A.; BROUKALOVÁ, I.: Structural Performance and crack control of fibre concrete beams with conventinal reinforcement. In: Brandt, A., Li, V., Marshall, I. (Eds.), BMC8 Eighth International Symposium on Brittle Matrix Composites. Woodhead Publishing Limited, Cambridge. Warsaw, Poland, pp. 361–372. ISBN: 1-84569-031-1, 8389687-09-7. 22z A14: 1x1 = 1b. ELIÁŠ, J. (2007) Stanovení materiálových parametrů modelu vláknobetonu. In: JUNIORSTAV 2007, 9. odborná konference doktorského studia Fakulty stavební VUT v Brně. 26c; 11z; 26z A14: 1x1 = 1b.
Získání vědecké kvalifikace: Ph.D. 24. 9. 2004 obhájení doktorské disertační práce na téma Stochastic fracture mechanics (stochastická lomová mechanika). Práce byla oceněna Cenou Rektora VUT v Brně 2/2005 a mezinárodní cenou v soutěţi Trimo Research Award 2005, Společnost Trimo, Slovinsko. Získání národního grantu: Statistical aspects of structural size effect (Statistické aspekty vlivu velikosti konstrukcí). Klíčová osoba. Projekt výzkumu a vývoje MŠMT (Clutch): 1K04110. 9/2004 – 6/2007. A24: 6 b. Probabilistic nonlinear finite element method with h-adaptivity. Zodpovědný řešitel. Projekt Grantové agentury ČR: GACR 103/06/P086. 1/2006 – 12/2008. A24: 6 b. Experimentální laboratoř ústavu stavební mechaniky – FRVŠ 2164 (TypAa). Řešeno spolu s Doc. Ing. V. Salajkou, CSc. a ing. P. Frantíkem, PhD. Kapitálová dotace: 1 699 tKč 2007 – nyní A24: 6 b. Účast na řešení národního výzkumného úkolu, grantu (člen řešitelského týmu): nebodováno 1996 – 1997 Optimalizace stavebních konstrukcí metodou evolučních strategií. Grant Fondu VUT pro vědy a umění č. B 20/94. 1996 – 1997
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
Prediction of Concrete Carbonation (Predikce karbonatace betonu). Projekt Grantové agentury ČR: GACR 103/95/0781. 1994 – 1995. 1997 – 2002 Material models of concrete for the assessment of severe accidents in nuclear industry. (Materiálové modely betonu pro posouzení nadprojektových havárií jaderných elektráren) Projekt Grantové agentury ČR: GACR 103/97/K003. 2000 – 2002 Risk assessment of structures - the loss of load-bearing capacity and serviceability of thinwalled structures (Stanovení rizika ztráty únosnosti a provozuschopnosti stavebních konstrukcí), Projekt Grantové agentury ČR: GACR 03/00/0603 1997 – 2004 Theory, reliability and mechanism of damage statically and dynamically stressed structures (Teorie, spolehlivost a mechanismus porušování staticky a dynamicky namáhaných stavebních konstrukcí). Výzkumný záměr MSMT CEZ:J22/98:261100007. 2002 – 2004 Nonlinear Fracture Mechanics of Concrete with Utilization of Stochastic Finite Elements and Random Fields (Nelineární lomová mechanika betonu s vyuţitím stochastických konečných prvků a náhodných polí). Projekt Grantové agentury ČR: GACR 103/02/1030. 2004 – 2007 Model identification and optimization at material and structural levels. Projekt Grantové agentury ČR: GACR 103/04/2092. 2004 – 2007 VITESPO – Virtual testing of structural safety and reliability. (Virtuální testování bezpečnosti a spolehlivosti konstrukcí). Duration: 2.5 years. Společný projekt mezi Červenka Consulting Praha a VUT v Brně. Projekt Grantové agentury Akademie věd ČR: T409870411. 2006 CIDEAS Centrum integrovaného navrhování progresivních stavebních konstrukcí. Výzkumné centrum 1M6840770001. 9/2004 – 6/2007 Mechanismus porušení stavebních kompozitních materiálů s křehkou matricí namáhaných vysokými teplotami. Projektu výzkumu a vývoje MŠMT 1K04111 (Clutch). 2006 – nyní CIVAK Centrum integrovaného navrhování progresivních stavebních konstrukcí. Výzkumné centrum 1M6840770001. 2007 – 2009 Soft computing in structural mechanics (SCOME). Projekt Grantové agentury ČR: GACR 103/07/0760.
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
Účast na řešení mezinárodního projektu (člen řešitelského týmu): 01/2003 – 03/2003 Projekt Německé grantové agentury (DFG) v rámci ―Collaborative Research Center 532―. 03/2004 – 04/2004 Projekt Americké národní grantové agentury (U.S. National Science Foundation): CMS9713944 to Northwestern University NU (Prof. Z.P. Baţant). 10/2004 – 10/2006 Adaptive probabilistic modeling of localization, failure and size effect of quasi-brittle materials. Aachen University of technology, RWTH. Sponsored by DFG, Germany. 2000 – nyní SARA: Structural Analysis and Reliability Assessment, Mezinárodní projekt financovaný Brenner Autobahn, Itálie; realizováno ve spolupráci s Červenka Consulting, Praha, BOKU Vienna (Austria), TU Vienna (Austria), and University Trento (Italy). Členství v organizačním výboru národního nebo mezinárodního kongresu, sympózia, vědecké konference, semináře: 09/2000
Member of Local Organizing Committee: Workshop 3RE, Institute of Structural mechanics TUB together with Institute of Structural Mechanics, Weimar, Germany A22: 5 b.
02/2001
Chairman of section, Member of Local Organizing Committee: Brno University of Technology, Faculty of Civil Engineering, 3rd Scientific PhD international workshop. A22: 5 b.
06/2005
Member of Local Organizing Committee: 2nd International symposium Nontraditional cement & concrete. Brno University of Technology, Brno, Czech Republic A22: 5 b.
07/2006
Chairman of session ―Size effects‖: Alexandroupolis, Greece, 16th European Conference of Fracture. A22: 5 b.
Posudek k obhajobě disertační práce: nikdy
A30: 0 b.
Posudek domácí publikace: nikdy
A29: 0 b.
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Návrh na zahájení habilitačního řízení – P12
Ing. Miroslav Vořechovský, Ph.D.
B) Tvůrčí pedagogické působení Působení na vysoké škole: 2000 – 2006, tj. 6 let (50 %)
B1: 2 x 6 x 0,5 = 6 b.
Vyučované předměty: 0D1 Stavební mechanika I (cvičení), 0D2 Pruţnost a plasticita (cvičení) 0D3 Statika stavebních konstrukcí 1 (cvičení) BD01 Základy stavební mechaniky (cvičení) 5D4 Vybrané statě ze stavební mechaniky II (přednášky i cvičení) 5D5 Spolehlivost stavebních konstrukcí (přednášky i cvičení) 0D7 Nelineární mechanika (přednášky i cvičení) 6D0 Nelineární mechanika (přednášky i cvičení) BD02 Pruţnost a pevnost (přednášky v angličtině) D31 Spolehlivost stavebních konstrukcí (1) [Reliability of Structures] (přednášky a cvičení v angličtině) Vedení obhájené výborně klasifikované diplomové práce: Jan Eliáš Ceny: Hlávkova cena, cena děkana, 1. cena České komory autorizovaných inţenýrů činných ve výstavbě
B4: 2 b.
Vedení obhájené disertační práce: nikdy
B12: 0 b.
Členství v komisi pro státní doktorskou zkoušku: nikdy
B12: 0 b.
Členství v komisi pro obhajobu disertační práce: nikdy
B12: 0 b.
Členství v komisi pro státní závěrečnou zkoušku: nikdy
B13: 0 b.
Příprava koncepce nového předmětu: -
B12: 0 b.
Studentská vědecká a odborná činnost – fakultní: Vedení studenta Jan Eliáš, 2. místo s prací Stochastické modelování taţených a ohýbaných průřezů kompozitních materiálů vyztuţených vlákny Václav Sadílek, s prací Numerické modelování komplexního vlivu velikosti pomocí nelineární stochastické výpočtové mechaniky. Jiné pomůcky - software: RC-LifeTime – stochastická trvanlivost a ţivotnost ţelezobetonových kontrukcí 31/32
Návrh na zahájení habilitačního řízení – P12
FREET SMARTedt BUNDLE
Ing. Miroslav Vořechovský, Ph.D.
– statistická a citlivostní analýza výpočtového modelu metodami LHS apod. – úprava dat L-D diagramů – stochastická odezva taţených a ohýbaných kompozitů B9: 4*10 = 40 b.
Uchazeč je webmasterem anglické verze katederních internetových stránek. Nebodováno Učební texty pro studenty: (1s) VOŘECHOVSKÝ M. (2005). Pruţnost a plasticita CD03-MO1 Studijní opory pro studijní programy s kombinovanou formou studia, in Czech. B8: 2 b.
21. 5. 2007
32/32
…………………………………… Ing. Miroslav Vořechovský, Ph.D.
