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Received: 24 July 2018 Accepted: 31 August 2018 Explosives are used as a source of energy to break the rock mass. Majority of explosive energy is lost in the form of ground vibrations, noise, air blasts, etc. Blast-induced ground vibration is influenced by many parameters such as rock mass, explosive characteristics, blast design etc. The prediction of blast-induced ground vibration using regression analysis sometimes becomes too conservative leading difficulties in operating the mine efficiently and safely. Scaled distance approach to vibration prediction is still a very reliable predicting approach, but there are other alternative approaches which produce close results with a high value of correlation coefficient. There are modern tools for analysis and prediction which in many types of research proved to performed with more accuracy. ANN (Artificial Neural Network) is one which in fact is proved by many researchers in their papers to be an excellent prediction method of vibrations. Another method used is an ensemble learning method for classification, regression, and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class i.e. Random forest method. In this paper, it has been tried to predict the peak particle velocities for blasts at varying distances with different initiation system using Random forest, ANN, and scaled distance regression analysis approach. The correlation coefficients for each approach for different initiation system is obtained, higher values of correlation coefficients are obtained with increase in accuracy of initiation systems due to increase in actual charge per delay during blasting. Also, it has found that the prediction is more accurate while using ANN along with digital detonators.

mathematical modelling and engineering problems/mathematical modelling of engineering problems

Influence of initiation system on blast-induced ground vibration using random forest algorithm, artificial neural network, and scaled distance analysis