Prediction of bench blast-induced ground vibrations using Random Forest algorithm.
DOI:
https://doi.org/10.17159/Abstract
Highly accurate estimation of peak particle velocity (PPV) is crucial and has an undeniable impact on design of bench blasting operations in open pit mining projects, since the vibrations caused by blasting can significantly affect the integrity of nearby buildings and similar structures. Conventional models have since been used to predict these blast induced vibrations. These models are not capable of capturing nonlinear relationships between the several blasting-related parameters involved during blasting. Soft computing techniques can effectively model these complexities. In this paper, Random Forest algorithm was used to develop a model to predict blast-induced ground vibrations on bench blasting using 48 data records. The model was trained and tested using WEKA data mining software. To build this model, a feature selection process using several combinations of Attribute Evaluators and Search Methods under the WEKA Select Attributes tab was performed. The correlation coefficient of the actual data and model predicted data was 0.95 and the average error percentage was 22%. The Random Forest model performance was also compared to the equivalent–path-based (EPB) equation on the training set and it was seen that the Random Forest model can effectively be used to predict PPV.
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