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.
Funder
National Natural Science Foundation Project of China
Fundamental Research Funds for Central Universities of the Central South University
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
63 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献