Development of a probabilistic seismic performance assessment model of slope using machine learning methods

Shinyoung Kwag, Daegi Hahm, Minkyu Kim, Seunghyun Eem

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish
Article number3269
JournalSustainability (Switzerland)
Issue number8
StatePublished - 1 Apr 2020


  • Artificial neural network (ANN)
  • Gaussian process regression (GPR)
  • Machine learning methods
  • Slope seismic performance
  • Support vector machine (SVM)


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