采矿与岩层控制工程学报2025，Vol.7Issue(4)：75-91,17.DOI:10.13532/j.jmsce.cn10-1638/td.2024-1377
基于机器学习的巷道围岩变形融合分析及预测模型
Fusion analysis and prediction model of roadway deformation based on machine learning
摘要
Abstract
To predict deformation and locate damaged areas of roadways,a numerical model for the surrounding rock in a roadway with multiple disturbances on the floor was established.A dataset of roof deformation of the bottom drainage roadway with different geological parameters,such as surrounding rock strength and lateral pressure coefficient,and mining parameters,such as roadway section,drainage borehole and support strength was obtained.Machine learning algorithms,such as random forest,extremely randomized trees,GBDT and XGBoost were used to establish single-based learner roadway deformation prediction models respectively.With the elastic net algorithm as the meta-learner and using the Stacking fusion method,the output models of different based learners were fused to construct a fusion prediction model for surrounding rock deformation of the bottom drainage roadway under multiple disturbances.The inhibiting or promoting effects of various characteristic factors on roadway deformation were evaluated,and the dominant controlling factors affecting the stability of the surrounding rock in the bottom drainage roadway were identified.The bottom drainage roadway of the transportation roadway No.14040 in Zhaogu No.2 Mine was chosen as the engineering background.Using the established roadway deformation prediction model,with the actual production geological conditions and mining parameters of the roadway as input,the recommended support strength for the roadway was determined through reverse calculation by setting the desired roadway deformation,which guided the on-site roadway support design and key parameter determination.After field implementation of the recommended support strength,the roadway deformaitn is controlled within the allowable deformation range as specified by the decision-making model.The roof deformation is only 52%of the original support,effectively controlling the large deformation of surrounding rock in roadways.The roadway deformation prediction model established based on machine learning provides a new approach for roadway stability maintenance,promoting the development of intelligent operation and maintenance technology for coal mine roadways.关键词
巷道变形/机器学习/预测模型/智能运维/围岩控制Key words
roadway deformation/machine learning/prediction model/intelligent operation and maintenance/surrounding rock control分类
矿业与冶金引用本文复制引用
王猛,袁春玉,李鑫磊,胡超,袁瑞甫,尚栋煌,王成..基于机器学习的巷道围岩变形融合分析及预测模型[J].采矿与岩层控制工程学报,2025,7(4):75-91,17.基金项目
国家自然科学基金资助项目(52174074) （52174074）
河南省自然科学基金资助项目(222300420048) （222300420048）
河南省高校创新人才资助项目(23HASTIT012) （23HASTIT012）
