Prediction of Peak Particle Velocity of Blast-induced Ground Vibrations using Boosted Regression Trees Authored
Authors
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Mining Engineering Department, National Institute of Technology, Raipur, Chhattisgarh – 492010 ,IN
Rajesh Sonkar -
Mining Engineering Department, National Institute of Technology, Raipur, Chhattisgarh – 492010 ,INPrakash Y. Dhekne
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Electrical Engineering Department, National Institute of Technology, Raipur, Chhattisgarh – 492010 ,INNarendra D. Londhe
DOI:
https://doi.org/10.18311/jmmf/2022/30057Keywords:
Blast-induced Ground Vibrations, Boosted Regression Tree, Linear Regression, PPV Prediction Model, Stepwise RegressionAbstract
Loosening of rockmass during its excavation in an infrastructure project is carried by rock blasting. The blast-induced ground vibrations pose a major challenge to the blasting engineers, whose main objective is to control their potential to cause any damage to the buildings in the vicinity. The research reported in this paper explains how the error in the prediction of the Peak Particle Velocity (PPV) by the United States Bureau of Mines (USBM)-based approach can be minimised using machine learning techniques. The complex correlation between the blast parameter and the PPV value has been modelled using the least square boosted decision tree approach after the selection of the best suitable feature has been selected based on the correlation matrix. The proposed model automatically maps the input blast feature (SD) with the target PPV values by aggregating the decision of various weak learners. The generalization of the proposed model has been validated through a 5-fold cross-validation approach using a dataset comprising of two hundred blast records generated by monitoring the blasts at International airport site, Navi Mumbai, India. The assessment of the prognostic ability of the proposed model demonstrates that it has outperformed the USBM-based approach for PPV prediction. The results establish that the predictions by the proposed model are closer to the measured values than the other regression models.
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