No abstract

No abstract

No abstract

No abstract

No abstract

Blasting is still considered to be the most economical technique for rock excavation and displacement either on the surface or underground. The explosive energy, which fractures the rockmass is not fully utilized and only 20-30% of the energy is utilized in actual breakage of the rockmass, and the rest of the energy is spread in the form of ground vibration, air blast, flying rock, back break, etc. Air blast is considered to be one of the most detrimental side effects due to generation of noise. A generalized equation has been proposed by many researchers but due to its site specific constants, it cannot be used in other geo-mining conditions. In the present paper, an attempt has been made to predict the air blast using a neural network (NN) by incorporating the maximum charge per delay and distance between blast face to monitoring point. To investigate the appropriateness of this approach, the predictions by a NN are also compared with generalized equation of air overpressure and conventional statistical relations. For prediction of air overpressure, the data set has been taken from two different limestone mines for training of the network while validation of the network has been done by Magnesite mine data set. The network is trained by 41 datasets with 50 epochs and tested by 15 dataset. The correlation co-efficient determined by a NN was 0.9574 while correlation co-efficient were 0.3811 and 0.5258 by generalized equation and statistical analysis respectively. The Mean Absolute Percentage Error (MAPE) for a NN was 2.7437, whereas MAPE for generalized equation and statistical analysis were 8.6957 and 6.9179 respectively.

No abstract

No abstract