Estimation of Shear Strength Parameters from Easily-Collected Soil Physical Properties Using Bagging Learning Technique

Shear strength parameters, including cohesion and friction angle, are among the most crucial factors in soil mechanics, playing a pivotal role in the design and construction of engineering projects. This paper aims to estimate these essential soil shear strength parameters using an ensemble learning model. To achieve this, the current study employs the Random Forest (RF) model incorporating various physical parameters of soil, such as density (?), saturation degree (Sr), liquid limit (LL), silt content (SC), clay content (CC) to predict cohesion (c), and friction angle (?). In order to assess the predictive performance of the used model, this research used various metrics, including the mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R2), to evaluate the model’s accuracy. The results reveal that RF performs superior predictive capabilities. Furthermore, the proposed model prediction ability was compared to the previous empirical equations. The comparison results indicated that the prediction capability of RF outperforms the previously developed equations