Prediction of blast-induced ground vibration in dolomitic marble quarry using Z-number information and fuzzy cognitive map based neural network models - Shahab Hosseini; Abiodun Ismail Lawal; Francois Mulenga (Rock Mechanics Bulletin)
Blast-induced ground vibration (BIGV) is one of the detrimental environmental consequences of blasting operations in mining and civil engineering. Hence, accurate prediction of BIGV is highly imperative. Therefore, different novel artificial intelligence (AI) methods such as Bayesian regularized neural network (BRNN), Bayesian regularized causality-weighted neural network (BRCWNN) and Z-number-based Bayesian regularized causality-weighted neural network (Z-BRCWNN) are proposed in this study for the reliable prediction of BIGV in a dolomitic marble quarry using the obtained field data. The outcome of the proposed models is subjected to rigorous statistical analyses. The outcome of analyses revealed that the Z-BRCWNN model outperformed the other models with 70%, 82% and 82% threshold statistic values evaluated at the 5%, 10% and 15% confidence levels for the testing phase and 63%, 91% and 91% threshold values for the validation phase evaluated at the same levels as above. The sensitivity analysis conducted revealed that the distance from the measuring point to the blasting point (DI) has the highest influence on BIGV.