Structural collapse simulation under consideration of uncertainty – Improvement of numerical efficiency

Möller, Bernd; Liebscher, Marco; Schweizerhof, Karl; Mattern, Steffen; Blankenhorn, Gunther

doi:10.1016/j.compstruc.2008.04.011

Cited by 20 publications

(6 citation statements)

References 11 publications

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“…To alleviate this problem, different approaches can be adopted. Among these methods, ANNs have gained a prominent position (Chojaczyk, Teixeira, Neves, Cardoso, & Soares, 2015;Javidan et al, 2018;Mitropoulou & Papadrakakis, 2011;Möller et al, 2008). It has been shown that the multilayer feed forward network architecture can estimate functions with high nonlinearity (Cardoso, de Almeida, Dias, & Coelho, 2008).…”

Section: Ann Metamodelmentioning

confidence: 99%

“…As a powerful uncertainty model, fuzzy randomness has been implemented in many studies to quantify uncertain parameters and conduct fuzzy reliability analysis (Graf, Hoffmann, Möller, Sickert, & Steinigen, 2007;Möller, Beer, Graf, & Sickert, 2006;Möller, Liebscher, Schweizerhof, Mattern, & Blankenhorn, 2008;Wang, Ma, Zhang, & Liu, 2012). Besides these research contributions, few attempts have been made to formulate sensitivity indices for fuzzy random structural systems.…”

Section: Introductionmentioning

confidence: 99%

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Variance‐based global sensitivity analysis for fuzzy random structural systems

Javidan

Kim

2019

Computer aided Civil Eng

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There have been increasing advances in the sophisticated approaches like fuzzy randomness to handle different uncertainties in civil engineering; however, less attention has been paid to the formulation of a sensitivity analysis for fuzzy random structural systems. In this study, the main objective is to present the formulation of fuzzy Sobol sensitivity indices to quantify the influence of fuzzy random structural parameters. Meanwhile, uncertainty in derivation of limit states and acceptance criteria in collapse analysis is addressed briefly and treated using fuzzy model parameters. To show the application of the established sensitivity test, the collapse behavior of a steel moment frame subjected to sudden column removal is evaluated thoroughly. The proposed fuzzy sensitivity indices are determined for the problem and the overall influence of fuzzy acceptance criteria on the collapse assessment is shown using fragility analysis. The results show that the presented fuzzy sensitivity analysis can give detailed insight into the characteristics of fuzzy random systems, and the epistemic uncertainty in derivation of limit states can have significant effects on the reliability‐based collapse analysis. It is worth mentioning that to alleviate high computational demands in fuzzy probabilistic collapse analysis, a neural network metamodel is applied in conjunction with the genetic algorithm which is also of practical value to engineers and researchers.

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Section: Ann Metamodelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variance‐based global sensitivity analysis for fuzzy random structural systems

Javidan

Kim

2019

Computer aided Civil Eng

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“…The identification of these paths and the associated physical and mechanical background is essential to understand a mechanical system and to design it for a specific purpose. Branching points can be associated with mechanical effects such as local buckling in shell structures [8,14,24], multiple failure kinematics in structural collapse after blasting [13,21], initialization of subor super-harmonic vibrations [17] or even chaotic behavior [19]. Numerical approaches for respective investigations are provided, in particular, on the basis of finite element analysis [1,2].…”

Section: Introductionmentioning

confidence: 99%

Detection of branching points in noisy processes

Beer

Liebscher²

2009

Comput Mech

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Processes in engineering mechanics often contain branching points at which the system can follow different physical paths. In this paper a method for the detection of these branching points is proposed for processes that are affected by noise. It is assumed that a bundle of process records are available from numerical simulations or from experiments, and branching points are concealed by the noise of the process. The bundle of process records is then evaluated at a series of discrete values of the independent process coordinates. At each discrete point of the process, the associated point set of process values is investigated with the aid of cluster analysis. The detected branching points are verified with a recursive algorithm. The revealed information about the branching points can be used to identify the physical and mechanical background for the branching. This helps to better understand a mechanical system and to design it optimal for a specific purpose. The proposed method is demonstrated by means of both a numerical example and a practical example of a crashworthiness investigation.

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“…A method for the prediction of fuzzy structural responses, which operates on the basis of a fuzzy ARMA process simulation starting from imprecise measured data, is presented in [259]. Applications to the numerical simulation of the controlled demolition of structures by blasting are reported in [260,261].…”

Section: Fuzzy Probabilitiesmentioning

confidence: 99%

Imprecise probabilities in engineering analyses

Beer

Ferson

Kreinovich

2013

Mechanical Systems and Signal Processing

385

193

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Probabilistic uncertainty and imprecision in structural parameters and in environmental conditions and loads are challenging phenomena in engineering analyses. They require appropriate mathematical modeling and quantication to obtain realistic results when predicting the behavior and reliability of engineering structures and systems. But the modeling and quantication is complicated by the characteristics of the available information, which involves, for example, sparse data, poor measurements and subjective information. This raises the question whether the available information is sucient for probabilistic modeling or rather suggests a set-theoretical approach. The framework of imprecise probabilities provides a mathematical basis to deal with these problems which involve both probabilistic and non-probabilistic information. A common feature of the various concepts of imprecise probabilities is the consideration of an entire set of probabilistic models in one analysis. The theoretical dierences between the concepts mainly concern the mathematical description of the set of probabilistic models and the connection to the probabilistic models involved.This paper provides an overview on developments which involve imprecise probabilities for the solution of engineering problems. Evidence theory, probability bounds analysis with p-boxes, and fuzzy probabilities are discussed with emphasis on their key features and on their relationships to one another.

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Structural collapse simulation under consideration of uncertainty – Improvement of numerical efficiency

Cited by 20 publications

References 11 publications

Variance‐based global sensitivity analysis for fuzzy random structural systems

Variance‐based global sensitivity analysis for fuzzy random structural systems

Detection of branching points in noisy processes

Imprecise probabilities in engineering analyses

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