A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network

Abstract

The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict ground vibration induced by bench blasting. An artificial neural network (ANN) was combined with the firefly algorithm (FFA), abbreviated as an FFA-ANN model, for this objective. To develop the FFA-ANN model, an ANN model (i.e., ANN 5-16-20-1) was established first; its weights and biases were then optimized by the FFA. A classification and regression tree (CART), a k-nearest neighbor (KNN), and a support vector machine (SVM) were also developed to confirm the power of the proposed FFA-ANN model. Eighty-three blasting events at a quarry mine in Vietnam were investigated to assess the danger of ground vibration through the developed models. The quality of the developed models was assessed through root-mean-squared error, mean absolute error, coefficient of correlation (R2), and variance account for. A simple ranking method and color gradient technique were also applied to evaluate the performance of the models. The results of this study indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN). The results also demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced ground vibration.