采矿与岩层控制工程学报2025，Vol.7Issue(3)：101-114,14.DOI:10.13532/j.jmsce.cn10-1638/td.2024-1350
基于A-Star算法的深部煤层开采含断层底板突水路径智能识别研究
Intelligent identification of water inrush path of floor with fault based on A-Star algorithm in deep coal seam mining
摘要
Abstract
To predict the water inrush risk of floor in deep coal seam mining,theoretical analysis,similar physical modeling and other research methods are used to conduct the intelligent identification of floor water inrush path.The prediction contour of floor water inrush is obtained based on the water pressure-stress monitoring data during the advance of mining face.According to the water pressure and stress data of each monitoring station,the probability index of water inrush at each position is quantitatively calculated.It is found that the greater this probility index,the higher the risk of water inrush.Taking the spatial distribution of water inrush probability index as the constraint condition,the A-Star path planning algorithm is used for the effective identification of the spatial path of water-conducting fracture,and an intelligent identification system of water inrush path is developed.Finally,similar material simulation was conducted for validation purpose.It is found that the path characteristics planned by the proposed method are highly consistent with the actual water-conducting fracture evolution trajectory of the model,and are highly consistent with the electrical monitoring results,verifying the effectiveness of the A-Star algorithm in water inrush path identification.The research results provide a new technical approach for real-time monitoring and early warning of floor water inrush disaster and guarantee safety of intelligent mine under deep mining conditions.关键词
防治水/A-Star算法/底板突水/综合权重/突水概率指数Key words
water control/A-Star algorithm/floor water inrush/comprehensive weight/probability index of water inrush分类
矿山工程引用本文复制引用
孙文斌,孙志辉,刘红强,褚怡心..基于A-Star算法的深部煤层开采含断层底板突水路径智能识别研究[J].采矿与岩层控制工程学报,2025,7(3):101-114,14.基金项目
国家自然科学基金资助项目(52274131) （52274131）
泰山学者工程专项经费资助项目(tsqn202211152) （tsqn202211152）
煤炭资源高效开采与洁净利用国家重点实验室开放基金资助项目(2021-CMCU-KF017) （2021-CMCU-KF017）
