Random Forest Tree Based Approach for Blast Design in Surface Mine

Abstract

Blasting is one of the primary mining operations for extracting minerals and ores however, if not designed properly, may have a varying degree of environmental and socio-economic impact in and around mining areas. In Indian mining industry, blast designs are fundamentally based on the experience and capability of the blasting crew and its assessment is more qualitative in nature, based on conventional trial and error basis. With the change in site geology and geotechnical parameters, the blast design parameters also require alterations, which can be standardized with the development of an intelligent system such as neural network. In this paper, the concept of artificial neural network and random forest algorithm has been used for better blast designs. Over 120 blast results from an opencast coal mine have been used for prediction of burden and energy factor with blast hole diameter, bench height to stemming ratio, nature of strata and average fragment size as input parameters. Out of 120 data sets 85 data sets recorded at a surface coal mine was used to train the model and 20 for the validation. Co-efficient of determination and root mean square error was chosen as the indicators to identify the optimum neural network and random forest model. The root mean square values obtained for energy factor is 0.153 while it is 0.1947 for burden. Similarly, the RMSE values obtained using random forest tree algorithm is 0.48 for burden while 50.76 for energy factor. The results revealed that random forest tree network system has potential to design better blast that is not generic and can be a potential tool for blasting engineers to design optimum blast for the mines.