JOURNAL ARTICLE

Structural reliability and stochastic finite element methods

Muhannad AldosaryJinsheng WangChenfeng Li

Year: 2018 Journal:   Engineering Computations Vol: 35 (6)Pages: 2165-2214   Publisher: Emerald Publishing Limited

Abstract

Purpose This paper aims to provide a comprehensive review of uncertainty quantification methods supported by evidence-based comparison studies. Uncertainties are widely encountered in engineering practice, arising from such diverse sources as heterogeneity of materials, variability in measurement, lack of data and ambiguity in knowledge. Academia and industries have long been researching for uncertainty quantification (UQ) methods to quantitatively account for the effects of various input uncertainties on the system response. Despite the rich literature of relevant research, UQ is not an easy subject for novice researchers/practitioners, where many different methods and techniques coexist with inconsistent input/output requirements and analysis schemes. Design/methodology/approach This confusing status significantly hampers the research progress and practical application of UQ methods in engineering. In the context of engineering analysis, the research efforts of UQ are most focused in two largely separate research fields: structural reliability analysis (SRA) and stochastic finite element method (SFEM). This paper provides a state-of-the-art review of SRA and SFEM, covering both technology and application aspects. Moreover, unlike standard survey papers that focus primarily on description and explanation, a thorough and rigorous comparative study is performed to test all UQ methods reviewed in the paper on a common set of reprehensive examples. Findings Over 20 uncertainty quantification methods in the fields of structural reliability analysis and stochastic finite element methods are reviewed and rigorously tested on carefully designed numerical examples. They include FORM/SORM, importance sampling, subset simulation, response surface method, surrogate methods, polynomial chaos expansion, perturbation method, stochastic collocation method, etc. The review and comparison tests comment and conclude not only on accuracy and efficiency of each method but also their applicability in different types of uncertainty propagation problems. Originality/value The research fields of structural reliability analysis and stochastic finite element methods have largely been developed separately, although both tackle uncertainty quantification in engineering problems. For the first time, all major uncertainty quantification methods in both fields are reviewed and rigorously tested on a common set of examples. Critical opinions and concluding remarks are drawn from the rigorous comparative study, providing objective evidence-based information for further research and practical applications.

Keywords:
Uncertainty quantification Computer science Polynomial chaos Finite element method Context (archaeology) Reliability (semiconductor) Ambiguity Collocation (remote sensing) Management science Data mining Industrial engineering Mathematics Monte Carlo method Machine learning Engineering Statistics

Metrics

43
Cited By
2.98
FWCI (Field Weighted Citation Impact)
162
Refs
0.92
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Probabilistic and Robust Engineering Design
Social Sciences →  Decision Sciences →  Statistics, Probability and Uncertainty
Structural Response to Dynamic Loads
Physical Sciences →  Engineering →  Civil and Structural Engineering
Structural Health Monitoring Techniques
Physical Sciences →  Engineering →  Civil and Structural Engineering

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