Prediction of ground vibrations induced by bench blasting using the random forest algorithm

Abstract

The accurate estimation of peak particle velocity (PPV) is crucial during the design of bench blasting operations in open pit mines, since the vibrations caused by blasting can significantly affect the integrity of nearby buildings and other structures. Conventional models used to predict blast-induced vibrations are not capable of capturing nonlinear relationships between the different blasting-related parameters. Soft computing techniques, i.e., techniques that are founded on the principles of artificial intelligence, effectively model these complexities. In this paper, we use the random forest (RF) algorithm to develop a model to predict blast-induced ground vibrations from 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 RF model-predicted data was 0.95, and the weighted average of the relative absolute error (RAE) was 10.9%. The RF model performance was also compared to the equivalent-path-based (EPB) equation on the testing data-set, and it was seen that the RF model can effectively be used to predict PPV. The study also demonstrates that the EPB equation is a suitable empirical method for predicting PPV.

Experimental and Numerical Study on the Effect of Three-Hole Simultaneous Blasting Technology on Open-Pit Mine Bench Blasting

“…From the second row of holes, the charge per hole was calculated with Equation (11). By calculation, the charge per hole was 63.36 kg.…”

“…In the present design, it was taken as 60 kg Q  . Starting from the second row of holes, the charge per hole for each subsequent row of holes was calculated using Equation (11).…”

“…As a result, it was reported that the blast hole diameter is the most important factor influencing blasting outcomes. Dzimunya et al [11] used the random forest algorithm to develop a model for predicting the blast-induced ground vibrations from bench blasting using 48 dataset records. The model was trained and tested by employing the WEKA data-mining software (3.9.5).…”