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Combination of model reduction and adaptive subset simulation for structural reliability problems

  • A safe and robust design is a key criterion when building a structure or a component. Ensuring this criterion can either be performed by fullfilling prescribed safety margins, or by using a full probabilistic approach with a computation of the failure probability. The latter approach is particularly well suited for complex Problems with an interaction of different physical penomena that can be described in a numerical model. The bottleneck in this approach is the computational effort. Sampling methods such as Markov chain Monte Carlo methods are often used to evaluate the system reliability. Due to small failure probabilities (e.g. 10^6) and complex physical models with already and extensive computational effort for a single set of parameters, these methods a prohibitively expensive. The focus of this contribution is to demonstrate the advantages of combining model reduction techniques within the concept a variance reducing adaptive sampling procedures. In the developed method, aA safe and robust design is a key criterion when building a structure or a component. Ensuring this criterion can either be performed by fullfilling prescribed safety margins, or by using a full probabilistic approach with a computation of the failure probability. The latter approach is particularly well suited for complex Problems with an interaction of different physical penomena that can be described in a numerical model. The bottleneck in this approach is the computational effort. Sampling methods such as Markov chain Monte Carlo methods are often used to evaluate the system reliability. Due to small failure probabilities (e.g. 10^6) and complex physical models with already and extensive computational effort for a single set of parameters, these methods a prohibitively expensive. The focus of this contribution is to demonstrate the advantages of combining model reduction techniques within the concept a variance reducing adaptive sampling procedures. In the developed method, a modification of the adaptive subset simulation based on Papaioannou et al. 2015 is used and coupled with a limit state function based on Proper Generalized Decomposition (PGD) (Chinesta et al. 2011). In the subset simulation the failure probability is expressed as a product of larger conditional failure probabilities. The intermediate failure events are chosen as a decreasing sequence. Instead of solving each conditional probability with a Markov chain approach, an importance sampling approach is used. It is be shown that the accuracy of the estimation depends mainly on the number of samples in the last sub-problem. For model reduction, the PGD approach is used to solve the structural problem a priori for a given Parameter space (physical space plus all random parameters). The PGD approach results in an approximation of the problem output within a prescribed range of all input Parameters (load factor, material properties, ..). The approximation of the solution by a separated form allows an evaluation of the limit state function within the sampling algorithm with almost no cost. This coupled PGD – adaptive subset Simulation approach is used to estimate the failure probability of examples with different complexity. The convergence, the error propagation as well as the reduction in computational time is discussed.zeige mehrzeige weniger

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Metadaten
Autor*innen:Jörg F. UngerORCiD
Koautor*innen:Annika Robens-Radermacher
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2019
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.7 Modellierung und Simulation
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Finite Elemente Method (FEM); Model reduction; Proper Generalized Decomposition (PGD); Reliability analysis; Simulation
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Veranstaltung:UNCECOMP 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
Veranstaltungsort:Crete, Greece
Beginndatum der Veranstaltung:26.06.2019
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:16.07.2019
Referierte Publikation:Nein
Eingeladener Vortrag:Nein
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