In order to protect the surface ecological environment of the mining area and improve the prediction accuracy of surface subsidence deformation under repeated mining in karst mountainous areas.Firstly,a theoretical analysis was conducted on the characteristics of fragmentation and swelling of the overlying strata after coal seam extraction,as well as the mechanism of surface sliding,and the influencing factors of surface movement and deformation in mountainous areas caused by coal seam mining were determined.On the basis of the original prediction model of the maximum surface subsidence value under repeated mining on the flat ground,considering the superposition effect of surface landslide on the maximum surface subsidence value,the prediction model of the maximum surface subsidence value under repeated mining in mountainous areas was constructed.Secondly,combined with the method of numerical simulation,the influences of coal seam mining on surface subsidence deformation under different ground slopes were studied.Finally,the prediction models of maximum surface subsidence values under repeated mining in flat ground and mountainous areas were used to predict the surface subsidence deformation of goaf by probability integral method,and the prediction results were compared with the measured values.The results show that the prediction accuracy of the maximum surface subsidence value prediction model based on repeated mining in mountainous areas is 1.4 percentage points higher than that of the original prediction model,and the prediction accuracy of the horizontal movement value is 4.3 percentage points higher than that of the original prediction model.The results indicate that the prediction model of the maximum subsidence value under repeated mining in mountainous areas is practical and can provide a reference for the study of surface subsidence in similar mining areas.
关键词
重复采动/地表沉陷/喀斯特山区/地面坡度/沉陷预测模型
Key words
Repeated mining/Surface subsidence/Karst mountainous areas/Ground slope/Prediction model of surface subsidence
维普
万方数据