College of Energy and Mining Engineering, Shandong University of Science and Technology
Qiuji Coal Mine, Shandong Energy Xinwen Mining Group Co. , Ltd.
Yushujing Coal Mine, Shandong Energy Xinwen Mining Group Co. , Ltd.
State Key Laboratory of Coal Mining and Clean Utilization
National Demonstration Center for Experimental Mining Engineering Education, Shandong University of Science and Technology

基于黄河北煤田邱集煤矿现场注浆工程,通过XGboost、支持向量机、K近邻和BP人工神经网络四种算法,对灰岩定向钻孔的注浆效果进行科学分析。研究表明,XGboost、支持向量机、K近邻的模型精度均达不到0.9,而BP人工神经网络测试的拟合程度达0.93,使用现场数据测试准确度达0.9,证明了BP人工神经网络评价注浆效果的可行性和准确性;最后运用MATLAB提出了一种基于BP人工神经网络的注浆效果智能化评价方法,并根据模型制作了简单的演示平台,实现了注浆效果评价的智能化与快捷化。

Based on the grouting project of Qiuji Coal Mine in Huanghebei Coalfield, we scientifically analyzed the grouting effect of limestone directional drilling using XGboost, support vector machine, K-nearest neighbor and BP artificial neural network. The research result shows that, the model accuracy of XGboost, support vector machine and K nearest neighbor is less than 0. 9, while the fitting degree of BP artificial neural network test is 0. 93. The accuracy of field data test is 0. 9, which proves the feasibility and accuracy of BP artificial neural network in evaluating grouting effect. Finally, an intelligent evaluation method of grouting effect based on BP artificial neural network is proposed by MATLAB, and a simple demonstration platform is made according to the model, which realizes the intelligent and rapid evaluation of grouting effect, and has a scientific guiding role for the development of mine grouting effect evaluation theory and safe coal mining.

煤炭资源高效开采与洁净利用国家重点实验室资助项目(2021-CMCU-KF015);国家自然科学基金项目(51974172);山东省自然科学基金面上项目(ZR201911150152,ZR2019MEE084,ZR2022ME140);山东科技大学科研创新团队支持计划项目(2018TDJH102)

1 注浆效果评价指标的选取
1.1 吸水率和渗透系数
1.2 总注浆量和单位注浆量
1.3 浆液压力和注浆终压
2 注浆评价方法
2.1 检查孔法
2.2 注浆信息分析法
2.3 地球物理勘验法
3 评价模型建立
3.1 数据和可视化分析
3.2 机器学习模型
3.2.1 XGboost
3.2.2 支持向量机
3.2.3 K近邻
3.3 BP神经网络
3.3.1 BP神经网络原理
3.3.2 网络的设计
4 结果分析与简易平台搭建
4.1 机器学习结果
4.2 BP神经网络结果分析
4.3 简易系统搭建
5 结 论

李文昕, 贾东秀, 陈建刚, 等. 灰岩含水层定向钻孔注浆效果智能评价方法研究 [J]. 煤炭工程, 2024, 56(1): 63-69.