Probabilistic seismic analysis for liquefiable embankment through multi-fidelity codes approach

López-Caballero, Fernando

doi:10.1016/j.soildyn.2021.106849

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…However, in general, several extensive complex FE simulations are required during the lifespan of a mechanical system, which is often impractical. Low-/mid-fidelity FEAs, which are updated on the actual condition of the system via experimental measurements and update themselves rapidly and effectively, have been offered as alternatives to comprehensive simulations [18], [19]. Bartilson et al [20] explored natural frequency and mode shape sensitivities in structures with at least one plane of symmetry and distinct natural frequencies.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Surrogate Finite Element Model for Estimating Dynamic Response of Mechanical Systems

2023

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An efficient approach for improving the predictive understanding of dynamic mechanical system variability is developed in this work. The approach requires low model assessment time through the fitting of surrogate models. ML-based surrogate algorithms for finite element analysis (FEA) are developed in this study to accelerate FEA and prevent rerunning complex simulations. The research begins with an overview of the recent novelties in ML algorithms applied to finite element (FE) and other physics-based computational schemes. To predict the time-varying response variables, that is, the displacement of a twodimensional truss structure, a surrogate FE technique based on ML algorithms is developed. In this work, several ML regression algorithms, including decision trees (DTs) and deep neural networks, are developed, and their efficacies are compared. In this study, the ML-based surrogate FE models are able to effectively predict the response of the truss structure in two dimensions over the entire structure. Extreme gradientboosting DTs provide more precise outcomes and outperform other ML algorithms.INDEX TERMS Surrogate modeling, finite element analysis, mechanical system analysis, machine learning, artificial neural networks, random forest trees, gradient boosting regression trees, adaptive boosting trees.

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Section: Introductionmentioning

confidence: 99%