Prediction of blast-produced ground vibration using particle swarm optimization

Blasting operation is an inseparable operation of the rock fragmentation process in the surface mines and tunneling projects. Ground vibration is one of the most undesirable effects induced by blasting operation which can cause damage to the surrounding residents and structures. So, the ability to make precise predictions of ground vibration is very important to reduce the environmental side effects caused by mine blasting. The aim of this paper is to develop a simple, accurate, and applicable model based on particle swarm optimization (PSO) approach for predicting the ground vibration induced by blasting operations in Shur River dam region, Iran. In this regard, two forms of PSO models, linear and power, were developed. For this work, a database including 80 data sets was collected, and the values of the maximum charge weight used per delay (W), distance between blast-point and monitoring station (D) and peak particle velocity (PPV) were measured. To develop the PSO models, PPV was used as output parameter, while W and D were used as input parameters. To check the performance of the proposed PSO models, multiple linear regression (MLR) model and United States Bureau of Mines (USBM) equation were also developed. Accuracy of models established was evaluated using statistical criteria, i.e., coefficient of correlation (R2) and root mean square error (RMSE), variance absolute relative error (VARE) and Nash & Sutcliffe (NS). Finally, it was found that the PSO power form provided more accurate predictions in comparison with PSO linear form, MLR and USBM models.