August 2020
Volume 72    Issue 8

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.


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,


Follow these easy steps if you are an SME member:
  • Go to . Sign in with your email address and password.
  • Hover your mouse over “Publications and Resources” in the top banner. Click on “Mining, Metallurgy & Exploration (MME) Journal” in the pull-down menu.
  • Scroll down and click on the “Read the MME Journal Online” button, which will take you to the Springer site as an SME member who is eligible for free access. (To see published papers on the Springer site, click on “Browse Volumes & Issues” in the blue banner.)
If you are not an SME member, go to for paid access. Or join SME at