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Geomechanics and Engineering Volume 30, Number 2, July25 2022 , pages 187-199 DOI: https://doi.org/10.12989/gae.2022.30.2.187 |
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Adaptively selected autocorrelation structure-based Kriging metamodel for slope reliability analysis |
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Jing-Ze Li, Shao-He Zhang, Lei-Lei Liu, Jing-Jing Wu and Yung-Ming Cheng
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| Abstract | ||
| Kriging metamodel, as a flexible machine learning method for approximating deterministic analysis models of an engineering system, has been widely used for efficiently estimating slope reliability in recent years. However, the autocorrelation function (ACF), a key input to Kriging that affects the accuracy of reliability estimation, is usually selected based on empiricism. This paper proposes an adaption of the Kriging method, named as Genetic Algorithm optimized Whittle-Matérn Kriging (GAWMK), for addressing this issue. The non-classical two-parameter Whittle-Matérn (WM) function, which can represent different ACFs in the Matérn family by controlling a smoothness parameter, is adopted in GAWMK to avoid subjectively selecting ACFs. The genetic algorithm is used to optimize the WM model to adaptively select the optimal autocorrelation structure of the GAWMK model. Monte Carlo simulation is then performed based on GAWMK for a subsequent slope reliability analysis. Applications to one explicit analytical example and two slope examples are presented to illustrate and validate the proposed method. It is found that reliability results estimated by the Kriging models using randomly chosen ACFs might be biased. The proposed method performs reasonably well in slope reliability estimation. | ||
| Key Words | ||
| genetic algorithm; Kriging; reliability analysis; slope stability; Whittle-Matérn | ||
| Address | ||
| Jing-Ze Li, Shao-He Zhang and Lei-Lei Liu: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, P.R. China Jing-Jing Wu: College of Civil Engineering, Hunan University of Technology, Zhuzhou 412007, P.R. China Yung-Ming Cheng: School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, P.R. China | ||