Prediction of blast induced ground vibration and frequency using an artificial intelligent technique

Abstract

When an explosive detonates, the sudden change generates waves in the surrounding media resulting in ground vibrations. As the vibration passes surface structures, it induces vibration in those structures. The vibrations generated in blasting are transmitted through the ground as seismic body waves and surface waves. These shock waves can cause severe damage to the nearby structures or surrounding rock mass. There are different methods to determine the peak particle velocity and the corrsponding frequency such as by USBM predictor methods and multivariate regression analysis etc. These methods are cumbersome and time consuming. In the present investigation, Artificial Neural Network (ANN) technique was used for the prediction of peak particle velocity (ppv) and the concerned frequency. Two different neural networks were designed for the prediction of ppv and frequency. For both the netwooks ten input neurons, one hidden layer with five neurons and one output neuron was used. The number of training and testing data sets, from large scale open cast coal mines, were 210 and 20 respectively. To deal with the problem of overfitting of data, Bayesian regulation was used and the network was trained with 1500 training epochs. The coefficients of correlation among the predicted and observed values were high and encouraging and Mean Absolute Percentage Error (MAPE) obtained was very low.