Abstract
Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.
Highlights
In blasting, only 25% to 30% of the explosive energy is spent in rock mass fragmentation, and more than 70% is wasted and causes side effects such as back break, fly rock, and blast-induced ground vibration [1,2,3]
After optimal random forest (RF) model development, the testing datasets were inputted into the established RF model for prediction, the prediction performance shows the prediction performance with using the training datasets with R2 of 0.92, mean absolute error (MAE) of 0.19 and root mean square error (RMSE) of
Harris Hawks Optimization (HHO)‐RF model model were were developed developed for for peak particle velocity prediction, and the results obtained show that the model peak particle velocity prediction, and the results obtained show that the HHO‐RF model can can provide provide more accurate accurate prediction prediction performance
Summary
Only 25% to 30% of the explosive energy is spent in rock mass fragmentation, and more than 70% is wasted and causes side effects such as back break, fly rock, and blast-induced ground vibration [1,2,3] Among these blast-induced side effects, blast-induced ground vibration is considered as the most common, important and dangerous side effects for nearby structures, human life and the environment [4,5,6]. According to previous literature [7], the peak particle velocity is normally considered to be more important than frequency and always selected to describe the blast-induced ground vibration. Some studies show these empirical models can predict PPV in some mines with high
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