Optimization and Application of Mining Subsidence Dynamic Prediction Model Based on Segmented Knothe Time Function
The depth of the coal seam in the Huainan mining area reaches 800～1 200 m,and the propagation time of the mining influence is long.When the original segmented Knothe time function model is used to predict the dynamic process of surface subsidence in this area,there are the following problems:there is no initiation stage of the predicted surface subsid-ence,the surface subsidence value corresponding to the maximum subsidence rate is not equal to 1/2 of the maximum subsid-ence value,and the time-dependent influence coefficient(c)and the moment corresponding to the maximum sinking speed of ground points(τ)are not adaptively valued.Through theoretical research and data analysis,it is proposed to optimize the Knothe time function model by invoking the surface subsidence initiation time t0 and the correction model,and to construct the parameter c and τ solution model by combining the geological characteristics of the Huainan mining area and the relevant theo-ry of the probability integral model,so as to propose the optimization model of the segmented Knothe time function which is suitable for the Huainan thick alluvial layer mining area.Taking the 1613(3)working face of a mine in Huainan as an exam-ple,the optimized Knothe time function model proposed in this paper,the original segmented Knothe time function model and the segmented Knothe time function model are used to predict the surface subsidence.The results show that taking the meas-ured values of the surface points as a reference,the optimised model proposed in this paper has an predicted standard error of 295.8 mm,and the overall accuracy is improved by 49%over the original segmented Knothe time function and by 53%over the segmented Knothe time function,which proves the superiority of the proposed optimised model.
mining subsidenceKnothe time functiondynamic predictionmodel optimizationsubsidence speed
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万方数据
