Optimized developed artificial neural network-based models to predict the blast-induced ground vibration

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Blasting has been widely used as an accepted mechanism in mining, construction, and rock engineering projects. However, inappropriate control of the blasting-induced ground vibration as an inevitable side effect can cause severe problem for the nearby areas. Therefore, developing the predictive models to estimate the blasting-induced ground vibrations can be considered as an attractive practical issue in engineering projects both in designing and operational stages. In the present paper, blasting-induced ground vibration at Masjed Soleyman earth dam in southwest of Iran in terms of peak particle velocity (PPV) using two different artificial neural network (ANN)-based models has been assessed and predicted. The multilayer perceptron (MLP) and generalized feed forward neural network (GFNN) were developed and optimized using monitored blast records. The total charge, charge per delay, and distance from blasting point were the input parameters. The quality and performance of introduced ANN topologies were compared to known conventional empirical predictors and then examined by different statistical indices and sensitivity analyses criteria. Although both GFNN and MLP indicated higher degree of safety and reliability in prediction of PPV, but the validation process using the unseen randomized data highlighted better performance and more accuracy in GFNN model with respect to MLP and common empirical predictors. Therefore, the GFNN with 3-4-3-1 structure and R2 = 0.954 between the measured and predicted PPV values was recognized as the optimized developed structure for the studied area.