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

摘要

Blasting has been widely used as an accepted mechanism in mining, construction, and rock engineering projects. However,rninappropriate control of the blasting-induced ground vibration as an inevitable side effect can cause severe problem for thernnearby areas. Therefore, developing the predictive models to estimate the blasting-induced ground vibrations can be consideredrnas an attractive practical issue in engineering projects both in designing and operational stages. In the present paper,rnblasting-induced ground vibration at Masjed Soleyman earth dam in southwest of Iran in terms of peak particle velocityrn(PPV) using two different artificial neural network (ANN)-based models has been assessed and predicted. The multilayerrnperceptron (MLP) and generalized feed forward neural network (GFNN) were developed and optimized using monitoredrnblast records. The total charge, charge per delay, and distance from blasting point were the input parameters. The quality andrnperformance of introduced ANN topologies were compared to known conventional empirical predictors and then examinedrnby different statistical indices and sensitivity analyses criteria. Although both GFNN and MLP indicated higher degree ofrnsafety and reliability in prediction of PPV, but the validation process using the unseen randomized data highlighted betterrnperformance and more accuracy in GFNN model with respect to MLP and common empirical predictors. Therefore, thernGFNN with 3-4-3-1 structure and R~2 = 0.954 between the measured and predicted PPV values was recognized as the optimizedrndeveloped structure for the studied area.