Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods

Shinyoung Kwag, Daegi Hahm, Minkyu Kim and Seunghyun Eem
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Shinyoung Kwag: Department of Civil and Environmental Engineering, Hanbat National University, Daejeon 34158, Korea
Daegi Hahm: Mechanical and Structural Safety Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea
Minkyu Kim: Mechanical and Structural Safety Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea
Seunghyun Eem: School of Convergence & Fusion System Engineering, Kyungpook National University, Gyeongsanbuk-do 37224, Korea

Sustainability, 2020, vol. 12, issue 8, 1-22

Abstract: The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.

Keywords: slope seismic performance; machine learning methods; support vector machine (SVM); artificial neural network (ANN); Gaussian process regression (GPR) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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