Chaos Game Optimization-Hybridized Artificial Neural Network for Predicting Blast-Induced Ground Vibration

Abstract

In this study, we introduced the chaos game optimization-artificial neural network (CGO-ANN) model as a novel approach for predicting peak particle velocity (PPV) induced by mine blasting. The CGO-ANN model is compared with other established methods, including the particle swarm optimization-artificial neural network (PSO-ANN), the genetic algorithm-artificial neural network (GA-ANN), single ANN, and the USBM empirical model. The aim is to demonstrate the superiority of the CGO-ANN model for PPV prediction. Utilizing a dataset comprising 180 blasting events from the Tonglushan Copper Mine in China, we investigated the performance of each model. The results showed that the CGO-ANN model outperforms other models in terms of prediction accuracy and robustness. This study highlights the effectiveness of the CGO-ANN model as a promising tool for PPV prediction in mining operations, contributing to safer and more efficient blasting practices.

Keywords

• peak particle velocity (PPV)
• chaos game optimization (CGO)
• artificial neural network (ANN)
• mine blasting
• prediction model
• Tonglushan Copper Mine