Toward disaster management in rock engineering: Automated machine learning paradigm for predicting the uniaxial compressive strength of rock materials

The uniaxial compressive strength (UCS) of rocks is a crucial indicator for evaluating the bearing capacity of geological structures in rock engineering, and it holds significant implications for disaster management. However, direct measurement poses a significant challenge. Therefore, simpler alternatives such as Schmidt hammer rebound number (SRn), P-wave velocity (Vp), and point load index (Is) are frequently used to estimate UCS indirectly. In this study, we compiled a comprehensive dataset of 1168 samples that included SRn, Vp, Is, and UCS values. The dataset was refined using an isolation forest algorithm, which identified and removed 280 outliers, leaving a dataset of 888 samples for analysis. We developed and assessed an automated machine learning (AutoML) model for predicting UCS, introducing a novel approach to tackle this prediction challenge. Additionally, we compared models enhanced by Bayesian optimization, including multi-layer perceptron (MLP), support vector machine (SVM), Gaussian process regression (GPR), and K-nearest neighbor (KNN). Among these, the AutoML model demonstrated superior performance in UCS prediction, offering a rapid and efficient method for estimating UCS in engineering applications and enabling intelligent classification of rock masses. The study also evaluated the sensitivity and contribution of SRn, Vp, and Is in UCS estimation by various techniques, including permutation feature importance (PFI), SHapley Additive exPlanations (SHAP), and local interpretable model-agnostic explanations (LIME). The results underscore that the AutoML approach not only streamlines UCS modeling but also provides a robust and comprehensive solution, significantly enhancing the accuracy and efficiency of the prediction process.