Monte Carlo and variance reduction methods for structural reliability analysis: A comprehensive review

• Published in: Probabilistic engineering mechanics Vol. 73; p. 103479
• Main Authors: Song, Chenxiao, Kawai, Reiichiro
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Monte Carlo methods; Structural reliability; Surrogate models; Variance reduction; Structural reliability; Monte Carlo methods; Surrogate models; Variance reduction
• ISSN: 0266-8920
• DOI: 10.1016/j.probengmech.2023.103479

Monte Carlo methods have attracted constant and even increasing attention in structural reliability analysis with a wide variety of developments seamlessly presented over decades. Along the way, a number of specialized reviews and benchmark studies have been provided from time to time, aiming at summarizing and comparing selected few approaches in detail, mainly from an implementation point of view. In contrast, the aim of the present survey is to play a comprehensive role as a methodological guidebook on Monte Carlo simulation and its related, especially variance reduction, techniques through a covering of 444 references in the relevant literature. To achieve this goal, we present an extensive review of formulations and techniques along with insightful summaries of developments of existing numerical methods, ranging from the general formulation, sub-categories and variants, to their combined uses with other simulation techniques and surrogate models, as well as the key advantages and assumptions. •Methodological overview of Monte Carlo methods in structural reliability analysis.•Exhaustive review of formulations with an emphasis on variance reduction techniques.•Extensive survey of related approximate and surrogate methods.•Insights for selecting Monte Carlo techniques and improving implementation.