Structural reliability analysis under evidence theory using the active learning kriging model. Issue 11 (2nd November 2017)

• Record Type: Journal Article
• Authors: Yang, Xufeng; Liu, Yongshou; Ma, Panke
• Is Part Of: Engineering optimization. Volume 49:Issue 11(2017)
• Page Start: 1922
• Page End: 1938
• Publication Date: 2017-11-02
• Subjects: Reliability analysis -- evidence theory -- active learning -- kriging model; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2016.1277063
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush–Kuhn–Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.