Blast induced air overpressure and its prediction using artificial neural network

Air blast is considered to be one of the most hazardous environmental disturbances created by blasting operation. Prediction of air overpressure (AOP) generated owing to blasting is difficult due to the influence of several factors in the air wave transmission. Blast design parameters, wind direction and speed, atmospheric temperature, humidity and topography, etc. are all affecting AOP. In this paper, an attempt has been made to predict AOP using artificial neural network (ANN) by incorporating the most influential parameters like maximum charge weight per delay, depth of burial of charge, total charge fired in a round and distance of measurement. To investigate the effectiveness of this approach, the predicted values of AOP by ANN were compared with those predicted by generalised equation incorporating maximum charge weight per delay and distance of measurement. Air overpressure data sets obtained from four different mines in India were used for the neural network as well as to form generalised equation. The network was trained by 70 data sets and validated with 25 data sets. The network and generalized predictor equations were tested with 15 AOP data sets obtained from another two mines. The results obtained from neural network analysis showed that the depth of burial of the charges and maximum charge weight per delay were among the blast designed parameters that have most influence on AOP. Based on the ANN result, depth of burial of charge has more relative sensitivity and weight than the maximum charge weight per delay. The average percentage of prediction error for ANN was 2.05, whereas for generalised equation, it was 5.97. The relationship between measured and the predicted values of AOP was found to be more logical in the case of ANN (correlation coefficient: 0.931) than that of generalised equation (correlation coefficient: 0.867)