Monte Carlo methods have attracted constant and even increasing attention in structural
reliability analysis with a wide variety of developments seamlessly presented over decades …

Abstract Design of structural and multidisciplinary systems under uncertainties requires
estimation of their reliability or equivalently the probability of failure under the given …

Bayesian active learning methods have emerged for structural reliability analysis and shown
more attractive features than existing active learning methods. However, it remains a …

Line sampling (LS) has proved to be a highly promising advanced simulation technique for
assessing small failure probabilities. Despite the great interest in practical engineering …

Line sampling has been demonstrated to be a promising simulation method for structural
reliability analysis, especially for assessing small failure probabilities. However, its practical …

Line sampling (LS) stands as a powerful stochastic simulation method for structural reliability
analysis, especially for assessing small failure probabilities. To further improve the …

The Bayesian failure probability inference (BFPI) framework provides a well-established
Bayesian approach to quantifying our epistemic uncertainty about the failure probability …

A Bayesian reinforcement learning reliability method that combines Bayesian inference for
the failure probability estimation and reinforcement learning-guided sequential experimental …

Preventive measures must be implemented to preclude severe damage to bridge structures
and thus avoid catastrophic accidents. A potential method for bridge damage identification is …

The Bayesian failure probability inference (BFPI) framework provides a sound basis for
developing new Bayesian active learning reliability analysis methods. However, it is still …