Open AccessStructural Reliability Analysis Using Genetic Algorithm and Gaussian Process Regression
Author(s) -
Yanjie Xiao,
Xun’an Zhang,
Feng Yue
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/783/1/012066
Subject(s) - reliability (semiconductor) , kriging , surrogate model , computer science , algorithm , monte carlo method , process (computing) , function (biology) , limit (mathematics) , gaussian process , gaussian , genetic algorithm , mathematics , machine learning , statistics , mathematical analysis , power (physics) , physics , quantum mechanics , evolutionary biology , biology , operating system
The implicit and computationally time-consuming performance function limits the application of classical reliability analysis methods in complex structures. To facilitate the reliability calculation of civil engineering structures, a reliability analysis method based on genetic algorithm (GA) and Gaussian process regression (GPR) is proposed in this paper. In this method, GPR is adopted to build the surrogate model of performance function, and GA is used for infill-sampling to improve the model accuracy at the limit state surface. Replacing the actual function with this model in Monte Carlo simulation (MCS), the approximate failure probability can be obtained. Four examples are analysed to validate the efficiency and accuracy of the proposed method. The results show that it can deal with the problems of static reliability and seismic reliability, and can be well combined with structural analysis software, which is convenient for engineering designers to use.