Geomechanics and Engineering

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Adaptively selected autocorrelation structure-based Kriging metamodel for slope reliability analysis

  • Li, Jing-Ze (Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University) ;
  • Zhang, Shao-He (Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University) ;
  • Liu, Lei-Lei (Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University) ;
  • Wu, Jing-Jing (College of Civil Engineering, Hunan University of Technology) ;
  • Cheng, Yung-Ming (School of Civil Engineering, Qingdao University of Technology)
  • Received : 2021.11.20
  • Accepted : 2022.06.11
  • Published : 2022.07.25
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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.

Keywords

Acknowledgement

The work described in this paper was funded by grants from the National Natural Science Foundation of China (Project No. 41902291), the Natural Science Foundation of Hunan Province, China (Project No. 2020JJ5704), and the Open Research Fund Program of Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education (Project No. 2020YSJS21), and the Fundamental Research Funds for Central Universities of the Central South University (Project No. 2021zzts0268). The financial support is greatly acknowledged.

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