Study on influencing factors and prediction of peak particle velocity induced by roof pre-split blasting in underground

作     者：Jiliang Kan Linming Dou Xuwei Li Jinrong Cao Jinzheng Bai Yanjiang Chai 

作者机构：Key Laboratory of Deep Coal Resource MiningMinistry of EducationChina University of Mining and TechnologyXuzhou 221116China School of MinesChina University of Mining and TechnologyXuzhou 221116China 

出 版 物：《Underground Space》 (地下空间（英文）)

年 卷 期：2022年第7卷第6期

页      面：1068-1085页

核心收录：

学科分类：081901[工学-采矿工程] 0819[工学-矿业工程] 08[工学] 0814[工学-土木工程] 

基　　金：the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX21_2378) National Natural Science Foundation of China(Grant Nos.51874292 and 51804303) 

主　　题：Roof pre-split blasting Peak particle velocity GA-ANN model Sensitivity analysis 

摘      要：Blasting technology is widely used to prevent coal bursts by presplitting the overburden in underground coal *** control of blasting intensity is important in achieving the optimal pre-split effectiveness and reducing the damage to roadway structures that are subjected to blasting *** a critical parameter to measure the blasting intensity,the peak particle velocity(PPV)of vibration induced by blasting,should be accurately predicted,and can provide a useful guideline for the design of blasting parameters and the evaluation of the *** this paper,various factors that influence PPV,induced by roof pre-split blasting,were analyzed using engineering blasting experiments and numerical *** results showed that PPV was affected by many factors,including charge distribution design(total charge and maximum charge per hole),spacing of explosive centers,as well as propagation distance and *** parameters,average charge coefficient and spatial discretization coefficient were used to quantitatively characterize the influences of charge distribution and spacing of explosive centers on the PPV induced by roof pre-split ***,a model consisting of the combination of artificial neural network(ANN)and genetic algorithm(GA)was adopted to predict the PPV that was induced by roof presplit blasting.A total of 24 rounds of roof pre-split blasting experiments were carried out in a coal mine,and vibration signals were collected using a microseismic(MS)monitoring system to construct the neural network *** verify the efficiency of the proposed GA-ANN model,empirical correlations were applied to predict PPV for the same *** results showed that the GA-ANN model had superiority in predicting PPV compared to empirical ***,sensitivity analysis was performed to evaluate the impacts of input parameters on *** research results are of great significance to improve the prediction accuracy of PPV induced by roof pre-splitti