Support vector machine (SVM) is a powerful machine learning technique relying on the
structural risk minimization principle. The applications of SVM in structural reliability analysis …

Abstract This Primer examines Skilling's nested sampling algorithm for Bayesian inference
and, more broadly, multidimensional integration. The principles of nested sampling are …

This paper reviews the mathematical foundation of the importance sampling technique and
discusses two general classes of methods to construct the importance sampling density (or …

The accurate estimations of the failure probability with low-computational burden play a vital
role in structural reliability analyses. Due to high-calculation cost and time-consuming Monte …

The present work reviews the implementation of adaptive metamodeling for reliability
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …

Spatial variability of soil properties was rarely taken into account directly in traditional slope
stability analyses, rather some “average” or suitably “pessimistic” properties are assumed to …

Active learning methods have recently surged in the literature due to their ability to solve
complex structural reliability problems within an affordable computational cost. These …

Purpose As an advanced calculation methodology, reliability-based multidisciplinary design
optimization (RBMDO) has been widely acknowledged for the design problems of modern …

Computing the sensitivity vector in the traditional first order reliability method may provide
inaccurate reliability outcomes for discrete performance functions and inefficient …

Reliability analysis approach provides a rational means to quantitatively evaluate the safety
of geotechnical structures from a probabilistic perspective. However, it suffers from a known …