Publication Type
Journal Article
Publication Date (Issue Year)
2022
Journal Name
Sustainability
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 R2 of 0.951, followed by SVR (R2 = 0.87), CART (R2 = 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
Rsif Scholar Name
Gbetoglo Charles Komadja
Thematic Area
Minerals, Mining and Materials Engineering
Africa Host University (AHU)
African University of Science and Technology (AUST), Nigeria
Funding Statement
This work was supported by Council of Scientific and Industrial Research-Central Institute of Mining and Fuel Research (CSIR-CIMFR), Dhanbad, India. And supported by CSIR-The World Academy of Science (TWAS) and the Partnership for skills in Applied Sciences, Engineering and Technology (PASET).
Recommended Citation
Komadja, G. C., Rana, A., Glodji, L. A., Anye, V., Jadaun, G., Onwualu, P. A., & Sawmliana, C. (2022). Assessing Ground Vibration Caused by Rock Blasting in Surface Mines Using Machine-Learning Approaches: A Comparison of CART, SVR and MARS. Sustainability, 14 (17), 11060. https://doi.org/10.3390/su141711060