Deep neural network and whale optimization algorithm to assess flyrock induced by blasting

A wide variety of artificial intelligence methods have been utilized in the prediction of flyrock induced by blasting. This study focuses on developing a model based on deep neural network (DNN) which is an advanced version of artificial neural network (ANN) for the prediction of flyrock based on the data obtained from the Ulu Thiram quarry that is located in Malaysia. To evaluate and document the success and reliability of the new DNN model, an ANN model based on five different data categories from the established database, was also developed and then compared with the DNN model. Based on the obtained results [i.e. coefficient of determination (R-2) = 0.9829 and 0.9781, root mean square error (RMSE) = 8.2690 and 9.1119 for DNN and R-2 = 0.9093 and 0.8539, RMSE = 19.0795 and 25.05120 for ANN], a significant increase in predicting flyrock is achieved by developing this DNN predictive model. Then, the DNN model was selected as a function for optimizing flyrock by a powerful optimization technique namely whale optimization algorithm (WOA). The WOA was able to minimize the flyrock resulting from blasting and provide a suitable pattern for blasting operations in mines.

Automated summary

This study developed a deep neural network (DNN) model to predict flyrock induced by blasting, which showed a significant increase in prediction accuracy compared to an artificial neural network (ANN) model. The DNN model, optimized using the whale optimization algorithm (WOA), successfully minimized flyrock resulting from blasting and provided a suitable pattern for blasting operations in mines.