Developing a new hybrid-AI model to predict blast-induced backbreak

Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R (2)) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R (2) equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model.