Assessing Ground Vibration Caused by Rock Blasting in Surface Mines Using Machine-Learning Approaches: A Comparison of CART, SVR and MARS

Gbétoglo Charles Komadja (), Aditya Rana, Luc Adissin Glodji, Vitalis Anye, Gajendra Jadaun, Peter Azikiwe Onwualu and Chhangte Sawmliana
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Gbétoglo Charles Komadja: Department of Materials Science and Engineering, African University of Science and Technology, Abuja 900001, Nigeria
Aditya Rana: Rock Excavation Engineering Research Group, CSIR-Central Institute of Mining and Fuel Research, Barwa Road, Dhanbad 826001, India
Luc Adissin Glodji: Department of Earth Sciences, University of Abomey-Calavi, Cotonou 01 BP 526, Benin
Vitalis Anye: Department of Materials Science and Engineering, African University of Science and Technology, Abuja 900001, Nigeria
Gajendra Jadaun: Rock Excavation Engineering Research Group, CSIR-Central Institute of Mining and Fuel Research, Barwa Road, Dhanbad 826001, India
Peter Azikiwe Onwualu: Department of Materials Science and Engineering, African University of Science and Technology, Abuja 900001, Nigeria
Chhangte Sawmliana: Rock Excavation Engineering Research Group, CSIR-Central Institute of Mining and Fuel Research, Barwa Road, Dhanbad 826001, India

Sustainability, 2022, vol. 14, issue 17, 1-30

Abstract: Ground vibration induced by rock blasting is an unavoidable effect that may generate severe damages to structures and living communities. Peak particle velocity (PPV) is the key predictor for ground vibration. This study aims to develop a model to predict PPV in opencast mines. Two machine-learning techniques, including multivariate adaptive regression splines (MARS) and classification and regression tree (CART), which are easy to implement by field engineers, were investigated. The models were developed using a record of 1001 real blast-induced ground vibrations, with ten (10) corresponding blasting parameters from 34 opencast mines/quarries from India and Benin. The suitability of one technique over the other was tested by comparing the outcomes with the support vector regression (SVR) algorithm, multiple linear regression, and different empirical predictors using a Taylor diagram. The results showed that the MARS model outperformed other models in this study with lower error (RMSE = 0.227) and R 2 of 0.951, followed by SVR (R 2 = 0.87), CART (R 2 = 0.74) and empirical predictors. Based on the large-scale cases and input variables involved, the developed models should lead to better representative models of high generalization ability. The proposed MARS model can easily be implemented by field engineers for the prediction of blasting vibration with reasonable accuracy.

Keywords: mining; blasting; ground vibration; machine learning; multivariate adaptive regression splines (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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