Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN

The drilling and blasting technique is among the common techniques for excavating tunnels with different shapes and sizes. Nevertheless, due to the dynamic energy involved, the rock mass around the excavation zone experiences damage and reduction in stiffness and strength. One of the most common and important issues that occurs during the tunneling process is the overbreak which is defined as the surplus drilled section of the tunnel. It seems that prediction of overbreak before blasting operations is necessary to minimize the possible damages. This paper develops a new hybrid model, namely, an artificial bee colony (ABC)-artificial neural network (ANN) to predict overbreak. Considering the most important parameters on overbreak, many ABC-ANN models were constructed based on their effective parameters. A pre-developed ANN model was also developed for comparison. In order to evaluate the obtained results of this study, a new system, i.e., the color intensity rating (CIR), was introduced and established to select the best ABC-ANN and ANN models. As a result, the ABC-ANN receives a high level of accuracy in predicting overbreak induced by drilling and blasting. The coefficients of determination (R-2) for the ANN and ABC-ANN are 0.9121 and 0.9428, respectively, for training datasets. This revealed that the ABC-ANN model (as a new model in the field of this study) is the best one among the models developed in this study.