Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

Panagiotis G Asteris,Panayiotis C Roussis,Fariz Iskandar Mohd Rizal,Behrouz Gordan,Danial Jahed Armaghani,Maria Ferentinou,Mohammadreza Koopialipoor

doi:10.3390/app12031753

Abstract

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions.

Highlights

Geotechnical engineers often employ analytical and empirical methods in order to estimate the safety factor, based on design parameters and engineering properties, of soil or rock material
The results obtained from these models were subjected to several performance indicators: namely, accuracy, precision, recall, F1-score, and the area under ROC curve or AUC to determine which method was the most accurate and effective for slope stability classification
To achieve the aim of this study, tree-based models including decision tree (DT), random forest (RF), and AdaBoost were developed to classify the stability of 700 slopes (464 safe slopes and 236 unsafe slopes) under seismic condition, which were modelled and analysed in GeoStudio software

Summary

Introduction

Geotechnical engineers often employ analytical and empirical methods in order to estimate the safety factor, based on design parameters and engineering properties, of soil or rock material. Slope stability analysis is a standard practice in geotechnical engineering employed for the estimation of the stability of natural or man-made slopes such as embankments of highways, railways, earth dams, tailings, etc. The analysis of slope stability mainly involves the calculation of the factor of safety (FOS), which is defined as the ratio between shear strength and the acting shear stress. The key parameters that define the geometry of the slope (i.e., height and slope inclination) and the material properties (i.e., angle of internal friction, cohesion, and pore water pressure) influence the evaluation of stability of slopes [1,2,3]. The assessment of slope stability is usually performed using analytical techniques, such as the limit equilibrium method (LEM) and finite element methods

Objectives

Methods

Results

Conclusion