• Shahani, Niaz Muhammad
• Hassan, Fawad Ul
• Ullah, Barkat
• Ali, Rashid

Abstract

<jats:p>The safe and sustainable design of rock slopes, open-pit mines, tunnels, foundations, and underground excavations requires appropriate and reliable estimation of rock strength and deformation characteristics. Cohesion (ᵅ0) and angle of internal friction (ᵱ1) are the two key parameters widely used to characterize the shear strength of materials. Thus, the prediction of these parameters is essential to evaluate the deformation and stability of any rock formation. In this study, four advanced machine learning (ML)-based intelligent prediction models, namely Lasso regression (LR), ridge regression (RR), decision tree (DT), and support vector machine (SVM), were developed to predict ᵅ0 in (MPa) and ᵱ1 in (°), with P-wave velocity in (m/s), density in (gm/cc), UCS in (MPa), and tensile strength in (MPa) as input parameters. The actual dataset having 199 data points with no missing data was allocated identically for each model with 70% for training and 30% for testing purposes. To enhance the performance of the developed models, an iterative 5-fold cross-validation method was used. The coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and a10-index were used as performance metrics to evaluate the optimal prediction model. The results revealed the SVM to be a more efficient model in predicting ᵅ0 (R2 = 0.977) and ᵱ1 (R2 = 0.916) than LR (ᵅ0: R2 = 0.928 and ᵱ1: R2 = 0.606), RR (ᵅ0: R2 = 0.961 and ᵱ1: R2 = 0.822), and DT (ᵅ0: R2 = 0.934 and ᵱ1: R2 = 0.607) on the testing data. Furthermore, to check the level of accuracy of the SVM model, a sensitivity analysis was performed on the testing data. The results showed that UCS and tensile strength were the most influential parameters in predicting ᵅ0 and ᵱ1. The findings of this study contribute to long-term stability and deformation evaluation of rock masses in surface and subsurface rock excavations.</jats:p>

Topics

• density
• impedance spectroscopy
• surface
• strength
• tensile strength
• machine learning
• microwave-assisted extraction