*Author for correspondence Journal of Mines, Metals and Fuels, 70(4) : 203-213; 2022. DOI: 10.18311/jmmf/2022/30057 Print ISSN : 0022-2755 Journal of Mines, Metals and Fuels Contents available at: www.informaticsjournals.com/index.php/jmmf INCORPORATING INDIAN MINING JOURNAL The Longest Serving Technical Journal of the Industry, Research, Academia and Business of Ores, Minerals, Fuels, Renewables and Aggregates www.jmmf.info Special Volume: JOURNAL OF MINES, METALS AND FUELS Rajesh Sonkar 1* , Prakash Y. Dhekne 1 and Narendra D. Londhe 2 1 Mining Engineering Department, National Institute of Technology, Raipur, Chhattisgarh – 492010, India; rajeshsonkariocl@gmail.com 2 Electrical Engineering Department, National Institute of Technology, Raipur, Chhattisgarh – 492010, India Abstract 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 aggre- gating 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 pro- posed model are closer to the measured values than the other regression models. Keywords: Blast-induced Ground Vibrations, Boosted Regression Tree, Linear Regression, PPV Prediction Model, Stepwise Regression Prediction of Peak Particle Velocity of Blast-induced Ground Vibrations using Boosted Regression Trees Authored 1. Introduction e development of infrastructure is necessary for boosting the economy. As a result, many infrastructure construc- tion projects are underway, round the globe. Blasting is a widely accepted technique to loosen the hard rock before its excavation during the construction of infrastruc- ture projects. When an explosive is detonated, it releases energy. e useful energy, which is nearly thirty per cent of the total energy released, is utilized for rock breakage whereas the remaining energy manifests itself as fly rock, air over pressure, blast-induced ground vibration, etc. e important parameters of blast-induced ground vibrations, to assess their potential for causing damage, are the PPV, acceleration, and frequency. However, the PPV is fre- quently used to estimate the intensity of the blast-induced ground vibrations and the investigators have extensively applied it for modelling (Amiri, 2016). e phenomenon of ground vibrations is annoying to the population resid- ing in the houses near the blasting site. When the PPV of