岩土力学2024，Vol.45Issue(2)：577-587,11.DOI:10.16285/j.rsm.2023.0287
深部开采地表移动延续时间预测模型及其参数分析
Prediction model and parameter analysis of surface movement duration in deep coal mining
摘要
Abstract
This paper presents a theoretical prediction model for the surface movement duration in coal mining,which takes into account various factors such as coal seam mining height,average mining depth,loose layer thickness,bedrock layer thickness,and mining speed.The model is based on the improved Knothe time model and incorporates the definition of surface movement duration.Additionally,a method for determining the model parameters using the probability integration method is provided.To validate the rationality and accuracy of the prediction model,monitoring data from 24 deep working faces are utilized.The results demonstrate that the predicted surface movement duration aligns well with the monitoring results from the working faces.The mean absolute error is only 38 days,the root mean square error is only 47 days,and the mean absolute percentage error is only 9％.These values indicate a significantly lower prediction error compared to existing empirical models.The accuracy of the surface movement duration prediction model is confirmed.The study further reveals that the duration is influenced by coal seam mining height,average mining depth,loose layer thickness,bedrock layer thickness,and mining speed.Specifically,it increases nonlinearly with coal seam mining height,linearly with average mining depth,loose layer thickness,and bedrock layer thickness,but decreases nonlinearly with mining speed.This research provides theoretical guidance for evaluating the stability of surface movement and deformation in coal mining and formulating scientifically sound mining plans.关键词
地表移动延续时间/改进Knothe时间模型/预测/动态沉降/开采速度Key words
surface movement duration/improved Knothe time model/prediction/dynamic subsidence/mining speed分类
矿业与冶金引用本文复制引用
张亮亮,程桦,姚直书,王晓健..深部开采地表移动延续时间预测模型及其参数分析[J].岩土力学,2024,45(2):577-587,11.基金项目
安徽省高校科研资助项目(No.2023AH051203) （No.2023AH051203）
安徽理工大学高层次引进人才科研启动基金(No.2022yjrc32) （No.2022yjrc32）
国家自然科学基金(No.51874005).This work was supported by the Natural Science Research Project of Anhui Educational Committee(2023AH051203),the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology(2022yjrc32)and the National Natural Science Foundation of China(51874005). （No.51874005）
