Prediction of Peak Particle Velocity of Blast-induced Ground Vibrations using Boosted Regression Trees Authored

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 control their potential cause any damage buildings vicinity. research reported this paper explains how error prediction Peak Particle Velocity (PPV) United States Bureau Mines (USBM)-based approach can be minimised using machine learning techniques. complex correlation between blast parameter and PPV value has been modelled least square boosted decision tree after selection best suitable feature selected based on matrix. proposed model automatically maps input (SD) with target values aggregating various weak learners. generalization validated through 5-fold cross-validation dataset comprising two hundred records generated monitoring blasts at International airport site, Navi Mumbai, India. assessment prognostic ability demonstrates that it outperformed USBM-based for prediction. results establish predictions are closer measured than other regression models.