A Hybrid Random
Forest optimized with the Dolphin Swarm Algorithm for predicting P-Wave Velocity
of Sedimentary Rocks using Ball Mill Grinding Characteristics
Swamy V. Sahas, Kunar Mihir Bijay and Karra Ram Chandar
Disaster Advances; Vol. 18(5); 1-9; doi:
https://doi.org/10.25303/185da0109; (2025)
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
(Vp) 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
Vp 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.
