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 …

Structural Reliability analysis (SRA) is one of the prominent fields in civil and mechanical
engineering. However, an accurate SRA in most cases deals with complex and costly …

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

The main task of structural reliability analysis is to estimate failure probability of a studied
structure taking randomness of input variables into account. To consider structural behavior …

In this article, new efficient methods are presented to classify failure modes in reinforced
concrete columns. For this purpose, machine learning techniques were utilized with …

In reliability engineering, estimating the failure probability of a system is one of the most
challenging tasks. Since many applied engineering tasks are computationally expensive, it …

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

Support vector regression (SVR) based metamodel is a powerful mean to alleviate
computational challenge of Monte Carlo simulation (MCS) based reliability analysis of …

Various metamodeling approaches eg polynomial response surface method artificial neural
network, Kriging method etc. have been emerged as an effective alternative for solving …

Abstract System reliability analysis involving multiple failure modes is challenging when
performance functions are associated with time-consuming codes. This paper aims to …