Predicting Blast-Induced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest

Abstract

Blasting is the most popular method for rock fragmentation in open-pit mines. However, the side effects caused by blasting operations include ground vibration, air overpressure (AOp), fly rock, back-break, dust, and toxic are the critical factors which have a significant impact on the surrounding environment, especially AOp. In this paper, a robust artificial intelligence system was developed for predicting blast-induced AOp based on artificial neural networks (ANNs) and random forest (RF), code name ANNs-RF. Five ANN models were developed first; then, the RF algorithm was used to combine them. An empirical technique, ANN, and RF models were also developed to predict and compare with the ANNs-RF model. For this aim, 114 blasting events were recorded at the Nui Beo open-pit coal mine, Vietnam. The maximum explosive charge capacity, monitoring distance, vertical distance, powder factor, burden, spacing, and length of stemming were used as the input variables for predicting AOp. The quality of the models is evaluated by root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and a simple ranking method. The results indicated that the proposed ANNs-RF model was the most superior model with RMSE of 0.847, R2 of 0.985, MAE of 0.620 on testing dataset, and total ranking of 40. In contrast, the best ANN model yielded a slightly lower performance with RMSE of 1.184, R2 of 0.960, MAE of 0.809, and a total ranking of 39; the RF model yielded a performance with RMSE of 1.550, R2 of 0.939, MAE of 1.222, and total ranking of 22; the empirical model provided the lowest accuracy level with RMSE of 5.704, R2 of 0.429, MAE of 5.316 on the testing dataset, and total ranking of 6.