An efficient algorithm for time-dependent failure credibility by combining adaptive single-loop Kriging model with fuzzy
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RESEARCH PAPER
An efficient algorithm for time-dependent failure credibility by combining adaptive single-loop Kriging model with fuzzy simulation Xia Jiang 1 & Zhenzhou Lu 1 Received: 10 September 2019 / Revised: 13 April 2020 / Accepted: 15 April 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The time-dependent failure credibility (TDFC) can reasonably measure the safety level of the time-dependent structure under the fuzzy uncertainty, but the direct optimization algorithm to estimate the TDFC requires large computational cost and even results in locally optimal solutions. Therefore, an efficient method is proposed for estimating the TDFC by combining the fuzzy simulation and the single-loop Kriging model. In the proposed method, fuzzy inverse transformation theorem is firstly used to transform the estimation of the TDFC into a sample classification problem, in which the candidate sample pool generated by fuzzy simulation (FS) is classified into the failure group and the safety one. For improving the efficiency of the classification, a Kriging model is adaptively trained by an elaborate U-learning function in the candidate sample pool. After the candidate sample is divided into the failure group and the safety one by the convergent Kriging model, the TDFC can be estimated as a byproduct easily. The innovation of the proposed method includes two aspects: establishing the idea of the fuzzy simulation combined with the single-loop Kriging model to estimate TDFC efficiently and robustly, and designing an elaborate U-learning function to improve the efficiency of training the single-loop Kriging model. The presented examples validate the efficiency of the proposed method under the acceptable precision. Keywords Time-dependent failure credibility . Fuzzy uncertainty . Single-loop Kriging model . Fuzzy simulation
1 Introduction As the uncertainties exist widely in the structural system, it is necessary to fully consider the uncertainty in order to reasonably evaluate the safety degree of the structural system (Yun et al. 2018; Sexsmith 1999). When sufficient information is available to determine the characteristics of uncertainty, it can be described by randomness. The structural safety evaluation methods (Feng
Responsible Editor: Byeng D Youn Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00158-020-02609-0) contains supplementary material, which is available to authorized users. * Zhenzhou Lu [email protected] Xia Jiang [email protected] 1
School of Aeronautics Xi’an, Northwestern Polytechnical University, Xian 710072, Shaanxi, China
et al. 2018; Hu and Du 2013) based on the random uncertainty require a large amount of data to accurately describe the probability distribution characteristics of the random uncertainties. The structural safety evaluation based on the inaccurate probability distribution characteristics will completely deviate from its true value (Elishakoff and Ferracuti 2006). Therefore, when the information is inc
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