Optimizing an Artificial Intelligence Model to Predict The Tunnel Face Area after Blasting

In underground tunnel construction for mining, the drilling and blasting method is widely used due to its advantages, such as low cost, simple calculation and implementation, and applicability in various geological and hydrogeological conditions. The drilling and blasting method is also suitable for tunnels with different cross-sectional shapes. One parameter that significantly influences the effectiveness of the drilling and blasting method is the area of the tunnel face after blasting. In this study, 136 datasets of influencing parameters and the tunnel face area after blasting from the DeoCa tunnel construction project were used to develop an artificial neural network (ANN) model capable of predicting the tunnel face area after blasting. The paper developed an ANN model and proposed a hybrid model based on the ANN model combined with a genetic algorithm (GA) to predict the area of the tunnel face after blasting. The input variables for the models included the designed tunnel face area (Sd), the specific charge (SC), the average borehole length (L), and the rock mass rating (RMR) of the rock mass on the tunnel face. This paper demonstrates that the hybrid GA-ANN model provides more accurate calculations and predictions for the tunnel face area after blasting than the ANN model alone.