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Simple Truss Finite Element to Model FRCM Strengthened Specimens in Single Lap Shear Tests Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Structural Model Updating and Model Selection: Bayesian Inference Approach Based on Simulation

Author : Anis Ben Abdessalem

Published in: Proceedings of the International Conference of Steel and Composite for Engineering Structures

Publisher: Springer Nature Switzerland

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Abstract

Structural model updating, and model selection have been in the focus of intensive research for many decades and still represent a major challenge. In this paper, the Approximate Bayesian Computation (ABC) using a Nested Sampling (NS) technique is employed to deal with model updating and model selection issues. The proposed framework is based on simulations, it can update a single model but also to find the most likely model from a set of competing models. Moreover, instead of learning a single point estimates, the ABC-NS scheme learns a distribution over the unknown model parameters allowing us to quantify predictive uncertainty. The ABC framework offers the possibility to use different discrepancy metrics measuring the similarity between the measured modal data and the ones obtained from simulations. The performance and the robustness of the simulation-based inference procedure in structural dynamics are demonstrated through two numerical studies using modal data.

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previous chapter A New Analytical Model for FRCM Coupons
next chapter Damage Tolerance in Topologically Optimized Structures: Exploring Structural Integrity Through Worst-Case Damage Optimization
Appendix
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Metadata
Title
Structural Model Updating and Model Selection: Bayesian Inference Approach Based on Simulation
Author
Anis Ben Abdessalem
Copyright Year
2024
Book
Proceedings of the International Conference of Steel and Composite for Engineering Structures

Print ISBN: 978-3-031-57223-4

Electronic ISBN: 978-3-031-57224-1

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-57224-1_22

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