Predicting blast-induced ground vibrations in some Indian tunnels using decision tree
Rana, Aditya; Bhagat, N.K.; Jadaun, G.P.; Rukhaiyar, Saurav; Pain, Anindya; Singh, P.K.
ABSTRACT:
This study compares three different techniques — decision tree, artificial neural network (ANN) and multivariate regression analysis (MVRA) — for predicting blast-induced ground vibrations in some Indian tunneling projects. The models’ performance was also compared with site-specific conventional predictor equations. A database consisting of 137 vibration records was randomly divided into training and testing sets for model generation. The results indicate that the decision tree is best suited to predicting vibrations. Furthermore, the decision tree suggests that the intensity of near-field ground vibrations is mainly affected by the total charge fired in a round, whereas the intensity of far-field vibrations is governed by maximum charge per delay and charge per hole.
Full-text paper:
Mining, Metallurgy & Exploration (2020) 37:1039–1053, https://doi.org/10.1007/s42461-020-00205-w
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