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2020 Issue 10

Dynamic prediction of mining-induced subsidence for any surface point based on optimized time function


ZHANG Bing ZHAO Yuling CUI Ximin YUAN Debao LI Chunyi

Author Unit:

School of Resource and Environmental Science, Shijjazhuang University College of Geoscience and Surveying Engineering,China University of Mining & Technology-Bejing School of Mining and Geomatics,Hebei University of Engineering Handan Key Laboratory of Natural Resources Spatial Information School of Surveying and Landing Information Engineering,Henan Polytechnic University

Key Words:

any point on surface; subsidence basin; mining subsidence; dynamic prediction;
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Aiming at the theoretical deficiency of Knothe time function in dynamic prediction and the low prediction accuracy in the initial stage of surface subsidenc e,in order to fully understand the dynamic development and change process of surface subsidence in mining areas,master the dynamic deformation law of all important points on the surface improve the precision and pertinence of dynamic prediction,and give play to the role of dynamic prediction in guiding mine engineering practice,ba sed on the probability integral model,a calculation model and calculation program for predicting the dynamic subsidence and deformation for any point on the surface is established by using the optimized segmented Knothe time function. The parameters of the model have clear meaning and are easy to be got. At the same time,even w hen the surface monitoring points on the work face are arranged on the non-main section,the probability integral parameters can also be calculated by using the model according to the measured data. The first prediction example shows that the subsidence value of the two inflexion points of the subsidence basin is equal to 1/2 of the maximum subsidence value when the inflexion offset is taken into account,which is completely consistent with the theoretical disclosure. The dynamic development proc ess of surface subsidence basin and tilt deformation is clearly ilustrated by the dynamic prediction of surface subsidence and tilt at different time points. In addition,acc ording to the change process of the tilt 3 D map and its 2 D map,although the tilt value of the flat bottom part of the sunken basin is finally 0,it also goes through a drast ic change process from small to large,from large to small,and finally to 0. If only the static prediction after stability is carried out,this dynamic process cannot be reflecte d. The second prediction example shows that by comparing and analyzing the measured and dynamic predicted results of the surface monitoring points in each period 0 f the east No.1176 working face, and analyzing the sampling accuracy,it can be known that the minimum relative MSE is 5.6%,and the maximum is 14. 2% , according to st atistics,the relative MSE of prediction can be stable at about 8% ,which proves the reliability of the prediction accuracy of the model.
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June 15th,2022

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