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An intelligent response surface method for analyzing slope reliability based on Gaussian process regression
Author(s) -
Zhu Bin,
Pei Huafu,
Yang Qing
Publication year - 2019
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.2988
Subject(s) - kriging , reliability (semiconductor) , benchmark (surveying) , gaussian process , computer science , stability (learning theory) , gaussian , monte carlo method , process (computing) , algorithm , mathematical optimization , machine learning , mathematics , statistics , power (physics) , physics , geodesy , quantum mechanics , geography , operating system
Summary Problems in geotechnical engineering inevitably involve many uncertainties in the analysis. Reliability methods are important for evaluating slope stability and can take the uncertainties into consideration. In this paper, a novel intelligent response surface method is proposed in which a machine learning algorithm, namely Gaussian process regression, is used to approximate the high‐dimensional and highly nonlinear response hypersurface. An iterative algorithm is also proposed for updating the response surface dynamically by adding the new training point nearest to the limit state surface to the initial training database at each step. The proposed Gaussian process response surface method is used to analyze three different case studies to assess its validity and efficiency. Direct Monte Carlo simulation is also carried out in each case to serve as the benchmark. Comparing with other methods confirms the accuracy and efficiency of the novel intelligent response surface method, which requires fewer performance function calls and avoids the need to normalize the correlative non‐normal variables.