Geomechanics and Engineering

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Gaussian process regression model to predict factor of safety of slope stability

  • Arsalan, Mahmoodzadeh (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Hamid Reza, Nejati (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Nafiseh, Rezaie (Department of Civil Engineering, Faculty of Engineering, University of Qom) ;
  • Adil Hussein, Mohammed (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil) ;
  • Hawkar Hashim, Ibrahim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Mokhtar, Mohammadi (Department of Information Technology, College of Engineering and Computer Science, Lebanese French University) ;
  • Shima, Rashidi (Department of Computer Science, College of Science and Technology, University of Human Development)
  • Received : 2021.06.28
  • Accepted : 2022.11.15
  • Published : 2022.12.10
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Abstract

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R2 value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

Keywords

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