Structural reliability analysis by line sampling: A Bayesian active learning treatment

TL;DR: This study proposes three Bayesian active learning methods, "partially Bayesian active learning cubature" (PBALC), for structural reliability analysis with extremely small failure probabilities, leveraging the Bayesian failure probability inference framework for improved accuracy and efficiency.

TL;DR: This study proposes Bayesian active learning line sampling with log-normal process (BAL-LS-LP) for rare-event probability estimation, improving traditional line sampling with a non-negativity-constrained prior and efficient learning function and stopping criterion.

TL;DR: This study applies the AK-DA active learning approach to assess cumulative fatigue damage of floating wind turbine mooring lines, achieving 45x efficiency gain and <1% absolute error, facilitating offshore mooring system design.

TL;DR: This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems.

TL;DR: In this article, a fundamental analysis of the meaning of second-moment reliability in multivariate problems is presented, and the format described is entirely derived from one basic assumption concerning the measurement of reliability.

TL;DR: In this article, a set simulation approach is proposed to compute small failure probabilities encountered in reliability analysis of engineering systems, which can be expressed as a product of larger conditional failure probabilities by introducing intermediate failure events.

TL;DR: An iterative approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures in a more efficient way and is shown to be very efficient as the probability of failure obtained with AK-MCS is very accurate and this, for only a small number of calls to the performance function.