Abstract
ABSTRACTModelling and prediction of blast-induced ground vibration is a significant aspect of mining and civil engineering operations, as ground vibration has dire consequences on both the environment, mine production and successful implementation of engineering projects. This study proposes the Multivariate Adaptive Regression Splines (MARS) as a novel alternative technique to model and predict blast-induced ground vibration. The MARS approach was compared with three artificial neural network methods and four conventional ground vibration predictors. The statistical analyses revealed that the MARS produced the best performance and can successfully be used for the prediction of blast-induced ground vibration.
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