Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA

Air overpressure (AOp) is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, the present precise model for the prediction of AOp would be much beneficial to control the AOp. To this end, the present study proposes a new hybrid of group method of data handling (GMDH) and genetic algorithm (GA). In the other words, the GA is used to optimize the GMDH. The proposed GMDH-GA model was constructed, trained, and tested based on a collection of 84 actual datasets collected from the Shur river dam region. In the modeling, four input parameters were considered: maximum charge per delay, distance between the blasting point and monitoring station, powder factor and rock mass rating. The coefficient of determination (R-2), root mean square error (RMSE) and variance account for (VAF), as the statistical performance indices, were used to evaluate the accuracy of the proposed GMDH-GA model. Consequently, the results indicate that the predicted values using the GMDH-GA model are in excellent agreement with the actual data (with the R-2 of 0.988), which demonstrate the reliability of the GMDH-GA model.