Evidence theory-based reliability optimization design using polynomial chaos expansion

DOI10.1016/j.cma.2018.07.015zbMath1440.74277OpenAlexW2884382746WikidataQ129518420 ScholiaQ129518420MaRDI QIDQ1986648

Chong Wang, Hermann G. Matthies

Publication date: 9 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2018.07.015

zbMATH Keywords

evidence theorybelief and plausibility measureClenshaw-Curtis point-based collocation methodLegendre polynomial chaos expansion in support boxreliability analysis and optimization

Mathematics Subject Classification ID

Optimization of other properties in solid mechanics (74P10)

Related Items (12)

Evidence-theory-based uncertain parameter identification method for mechanical systems with imprecise information ⋮ Random model with fuzzy distribution parameters for hybrid uncertainty propagation in engineering systems ⋮ Bayesian prior information fusion for power law process via evidence theory ⋮ Advanced solution strategies for time-dependent reliability based design optimization ⋮ Mixed aleatory and epistemic uncertainty propagation using Dempster-Shafer theory ⋮ Reliability-Based Design Optimization for Design Problems with Random Fuzzy and Interval Uncertainties ⋮ An intelligent evidence-theory-based structural reliability analysis method based on convolutional neural network model ⋮ Non-parametric stochastic subset optimization for reliability-based importance ranking of bridges in transportation networks ⋮ Epistemic uncertainty-based reliability analysis for engineering system with hybrid evidence and fuzzy variables ⋮ A new reliability method for small failure probability problems by combining the adaptive importance sampling and surrogate models ⋮ A two-stage surrogate model for neo-Hookean problems based on adaptive proper orthogonal decomposition and hierarchical tensor approximation ⋮ Hybrid evidence-and-fuzzy uncertainty propagation under a dual-level analysis framework

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