A Hybrid Random Forest optimized with the Dolphin Swarm Algorithm for predicting P-Wave Velocity of Sedimentary Rocks using Ball Mill Grinding Characteristics

Abstract

Rock properties play a crucial role in mining, geotechnical engineering and various engineering projects. P-wave velocity helps in determining the quality and stability of rock masses, essential for tunnel excavation, slope stability and mining operations. P-wave velocity also provides critical input for designing foundations for dams, bridges and other rock structures. Accurate determination of P-wave velocity relies on high-quality samples. However, challenges such as preparation, cost and time constraints have prompted a growing reliance on computational methods for its prediction. Previous investigations predominantly leaned on laboratory-based tests and indirect methodologies for predicting rock properties including P-wave velocity. In contrast, this study introduces an innovative technique for predicting wave velocity (V<inf>p</inf>) of sedimentary rocks, particularly limestone using ball mill grinding characteristics throughout the grinding procedure, an unconventional yet effective approach. A hybrid random forest model optimized with dolphin swarm algorithm was developed to predict V<inf>p</inf> from grinding characteristics. The performance of the model in training and testing phases was assessed based on determination coefficients (R2), root mean-squared error (RMSE) and variance account for (VAF) which are 0.984, 96.204 m/s and 98.25% in training and 0.973, 102.32 m/s and 97.63% in testing phase respectively. © 2025, World Researchers Associations. All rights reserved.