Leveraging Machine Learning Regression Algorithms to Predict Mechanical Properties of Evaporitic Rocks From Their Physical Attributes

Evaluating the geotechnical properties of evaporitic rocks is crucial for infrastructure stability; however, traditional methods are costly and labour-intensive. In this study, machine learning (ML) regression algorithms were applied to predict four key mechanical parameters, namely, uniaxial compressive strength (UCS), point load index (PLI), indirect tensile strength (ITS), and Schmidt hardness value (SHV), based on the physical attributes of evaporitic rocks. A comprehensive laboratory analysis of 149 block samples from Abu Dhabi was performed to measure their physical (density, porosity, unit weight, water content, specific gravity, and void ratio) and mechanical properties. Nine ML models were trained (80:20 data split) and validated using R-squared (R²), mean absolute error (MAE), and root-mean square error (RMSE). Nonlinear correlations demonstrated strong relationships between the mechanical properties and physical attributes, such as saturated density (s) and natural unit weight (n). After feature selection, random forest and XGBoost outperformed the other models with exceptional accuracy for UCS prediction (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.95) and robust performance for ITS (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.84) and SHV (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.77). This study advances previous research by simultaneously predicting multiple mechanical properties, offering an efficient alternative to expensive laboratory tests. The study findings highlight the potential of ML for improving geotechnical workflows by enabling rapid data-driven examinations of rock durability and stability.