Evidence-theory-based Reliability Analysis Method Using Active-learning Kriging Model

doi:10.3901/JME.2024.02.356

Abstract

Abstract: An evidence-theory-based reliability analysis method using the active-learning Kriging model is proposed to effectively calculate the belief and plausibility of a structural failure. First, the input uncertainty space is discretized and the Latin hypercube sampling is employed to generate the initial training samples for the Kriging model. Then, an efficient method based on the Kriging model is developed to classify all focal elements (positive, negative or boundary elements). Also developed is the stopping criterion of the active learning procedure. Next, a learning function based on the probability of correctly predicting the sign of performance function is proposed to adaptively increase the training samples and refine the Kriging model in important domain. Finally, the Kriging model, which is usually not globally accurate, is used to classify all focal elements correctly and obtain the belief and plausibility of a structural failure. Numerical examples show that the proposed method can classify all the focal elements correctly and efficiently, thus obtaining exact belief and plausibility.

Key words: structural reliability, epistemic uncertainty, evidence theory, active learning, Kriging model

CLC Number:

TB114

WEI Xinpeng, YAO Zhongyang, BAO Wenli, ZHANG Zhe, JIANG Chao. Evidence-theory-based Reliability Analysis Method Using Active-learning Kriging Model[J]. Journal of Mechanical Engineering, 2024, 60(2): 356-368.

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