ASS-GPR: Adaptive Sequential Sampling Method Based on Gaussian Process Regression for Reliability Analysis of Complex Geotechnical Engineering
Publication: International Journal of Geomechanics
Volume 21, Issue 10
Abstract
Reliability analysis of complex geotechnical engineering is time-consuming since its performance function is highly nonlinear and implicit. In this paper, an adaptive sequential sampling metamodeling-based method is proposed to deal with such problems. Gaussian process regression (GPR), utilized to approximate the real performance function, is constructed by the initial design of experiments (DOEs). Based on the geometric meaning of the most probable point (MPP) in the first-order reliability method (FORM), the potential MPP, which is a point infinitely close to the limit-state surface and with the minimum distance to the origin while considering a distance constraint, is searched and added to the DOE to refine the GPR. Then, the Monte Carlo simulation (MCS) is adopted to evaluate the failure probability by the refined GPR. The above two procedures are repeated until the stopping criterion is reached. Three examples, including one mathematical example and two geotechnical engineering problems, are analyzed. The results show the proposed method requires fewer performance function calls and is an efficient, accurate, and robust reliability analysis method.
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Acknowledgments
This work was supported by the National Key R&D Program of China (2016YFC0401600 and 2016YFC0401900) and the National Natural Science Foundation of China (51309048).
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Published In
International Journal of Geomechanics
Volume 21 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 31, 2020
Accepted: May 30, 2021
Published online: Aug 5, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 5, 2022
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