Prediction of blasting induced air-overpressure using a radial basis function network with an additional hidden layer

Abstract

Blasting operations are the most conventional and frequently used rock breakage approach in the field of Civil and Mining Engineering. However, the side effects induced by blasting may cause severe damages to surrounding areas. Air-overpressure (AOp) is one of the side effects induced by blasting operations, which is defined as the air pressure wave generated by blasting operation that exceeds normal atmospheric pressure. It can result in potential structural damage and glass breaking and therefore needs to be well predicted and subsequently minimized. In this study, 76 sets of blasting data were collected to develop a predictive model to estimate AOp value. However, due to the small size of dataset, it is hard to determine the complexity of the model. Therefore, for the purpose of developing a machine learning model with appropriate complexity, a radial basis function network with an additional second hidden layer (RBF-2) is proposed, which is trained by incremental design principle and modified Levenberg–Marquardt algorithm. The performance of the proposed RBF-2 is compared with those of five other machine learning techniques, i.e., multilayer perceptron (MLP), RBF, MLP optimized by genetic algorithm (GA-MLP), multi adaptive regression spline (MARS) and random forest (RF). The results demonstrate that the proposed RBF-2 network outperforms other models with RMSE of 2.02/1.98, MAPE of 1.32%/1.40%, and R of 0.9828/0.9735 in training/testing stage. Findings revealed that the proposed RBF-2 network emerged as the most efficient, powerful and robust technique in predicting blast induced AOp compared with other machine learning models.