Abstract
Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R2), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance.
Highlights
Drilling and blasting are important parts of an effective method to excavate the hard rock in the mining industry
It is evident that all the models performed efficiently in predicting peak particle velocity (PPV) in terms of statistical indices
The R2 was found to be higher in the case of biogeography-based optimization (BBO)-artificial neural network (ANN) (R2 = 0.988) compared to the other seven models: DIRECT-ANN (R2 = 0.981), particle swarm optimization (PSO)-ANN (R2 = 0.972), minimax probability machine regression (MPMR) (R2 = 0.971), extreme learning machine (ELM) (R2 = 0.965), United States Bureau of Mines (USBM) (R2 = 0.747), Indian standard (R2 = 0.799), and Ambraseys–Hendron (R2 = 0.724)
Summary
Drilling and blasting are important parts of an effective method to excavate the hard rock in the mining industry. One of the fundamental problems induced by blasting is the ground vibration The ability to make accurate predictions of the ground vibration is a crucial need in this field. Body waves including the compressional P-wave, transverse S-wave, and surface waves including the Love wave (Q-wave) and the Rayleigh wave (R-wave) are generated.
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