Estimation of Blast-Induced Peak Particle Velocity through the Improved Weighted Random Forest Technique

Blasting is one of the primary aspects of the mining operations, and its environmental effects interfere with the safety of lives and property. Therefore, it is essential to accurately estimate the environmental impact of blasting, i.e., peak particle velocity (PPV). In this study, a regular random forest (RF) model was developed using 102 blasting samples that were collected from an open granite mine. The model inputs included six parameters, while the output is PPV. Then, to improve the performance of the regular RF model, five techniques, i.e., refined weights based on the accuracy of decision trees and the optimization of three metaheuristic algorithms, were proposed to enhance the predictive capability of the regular RF model. The results showed that all refined weighted RF models have better performance than the regular RF model. In particular, the refined weighted RF model using the whale optimization algorithm (WOA) showed the best performance. Moreover, the sensitivity analysis results revealed that the powder factor (PF) has the most significant impact on the prediction of the PPV in this project case, which means that the magnitude of the PPV can be managed by controlling the size of the PF.

Automated summary

This study developed a regular random forest model to accurately estimate the environmental impact of blasting. To enhance the model's performance, several techniques were proposed. The results showed that all refined weighted models outperformed the regular model, with the refined weighted RF model using the whale optimization algorithm performing the best. Sensitivity analysis revealed that the powder factor has the most significant impact on the prediction of peak particle velocity.