The effect of soil physical properties on predicting shear strength parameters based on comparing ensemble learning, deep learning, and support vector machine models

The shear strength (SS) of soil is a critical parameter utilized in the design of civil engineering projects. The SS parameters, including cohesion (c) and friction angle (o), can be determined through methods conducted either in the field or within a laboratory environment. However, the traditional method for determining SS parameters are not only costly but also time-consuming. Recently, the application of machine learning (ML) in geotechnical problems has received increasing attention. In order to select an appropriate ML model and assess the effect of physical properties on the SS of soil. This research endeavors to predict critical SS parameters of soil through the application of five machine learning (ML) models, integrating easily-available physical soil index, including specific gravity (G), saturation degree (Sr), liquid limit (LL), silt content (SC), and clay content (CC). The used ML techniques include Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). A range of metrics, encompassing the root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R) were used to measure the predictive efficacy of the employed models as well as compare the performance of the used ML models. The values of R2 range from 0.769 to 0.987 indicate that all ML models exhibit excellent predictive capabilities for estimating SS parameters, in which the XGBoost, and CNN techniques show outperforming results compared to the other models. The study uses decision tree feature importance (DTFI) and coefficient feature importance (CFI) techniques to investigate how various physical properties impact the predictive capabilities of the model and indicates that both G and LL have a substantial impact on the predictive accuracy of cohesion and friction angle.