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Rare event simulation for large-scale structures with local nonlinearities. (English) Zbl 1442.74197

Summary: When modeling large-scale structures, knowledge of the systems and their environment is often incomplete, i.e., wind or wave loads, and possible defects in the structural components, often referred to as uncertainty parameters, can have a significant impact. However, conventional simulations are deterministic and do not take such uncertainty into consideration, making structural reliability analysis, e.g. rare event simulation, necessary. In this work, uncertainties are assumed to be in the parameters of the models, such that the numerical formulation of the models can be parametrized to account for the uncertainties. As a result, a rare event simulation becomes a quantification of the impact of the uncertain parameters on a quantity of interest of the structure, and comprises an essential part of risk assessment. However, traditional parametrized simulations, i.e., through finite element methods (FEMs), for large-scale infrastructures, possibly with a moderate to high dimensional parameter domain and a fine mesh discretization, are computationally intractable due to the multi-query sampling required by the simulation. This becomes an even harder challenge when nonlinearities are present in the structural analysis. In this work, we adopt non-intrusive reduced order modeling and machine learning techniques as an enabling technique for different fidelity rare event simulations. We consider various techniques for risk assessment for large-scale structures with local nonlinearities and moderate to high dimensional parameter spaces, and provide a comparative study through several numerical examples to demonstrate the feasibility and effectiveness of the presented approaches.

MSC:

74R05 Brittle damage

Software:

redbKIT; rbMIT
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Full Text: DOI

References:

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