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巨厚砾岩下开采地表沉陷预计模型的改进
引用本文:栾元重,张铭鑫,庄艳,楚宪亮.巨厚砾岩下开采地表沉陷预计模型的改进[J].科学技术与工程,2020,20(5):1786-1791.
作者姓名:栾元重  张铭鑫  庄艳  楚宪亮
作者单位:山东科技大学测绘科学与工程学院,青岛266590;山东科技大学测绘科学与工程学院,青岛266590;山东科技大学测绘科学与工程学院,青岛266590;山东科技大学测绘科学与工程学院,青岛266590
摘    要:为了探究巨厚砾岩下开采的地表沉陷规律,结合关键层理论,通过数值模拟从垂直应力、塑性区、垂直位移3个方面分析覆岩内部稳定性及造成地表倾向下沉曲线偏态的原因,基于概率积分法,建立改进的地表沉陷预计模型。结果表明:关键层初次破断距为291 m,周期破断距为281 m;悬力臂是造成下沉曲线偏态的主要原因;相对于经典的概率积分沉陷预计模型,改进的地表沉陷预计模型相对误差减小114%。可见在巨厚砾岩层下开采地表沉陷预计中,改进的地表沉降预计模型具有较高的准确度和适用性。

关 键 词:开采沉陷  巨厚砾岩层  数值模拟  概率积分  改进模型
收稿时间:2019/6/12 0:00:00
修稿时间:2019/12/2 0:00:00

Improvement of Surface Subsidence Prediction Model for Mining Under Hugely-Thick Conglomerate
Luan Yuanzhong,Zhang Mingxin,Zhuang Yan and Chu Xianliang.Improvement of Surface Subsidence Prediction Model for Mining Under Hugely-Thick Conglomerate[J].Science Technology and Engineering,2020,20(5):1786-1791.
Authors:Luan Yuanzhong  Zhang Mingxin  Zhuang Yan and Chu Xianliang
Affiliation:School of Mapping Science and Engineering,Shandong University of Science and Technology,School of Mapping Science and Engineering,Shandong University of Science and Technology,School of Mapping Science and Engineering,Shandong University of Science and Technology,School of Mapping Science and Engineering,Shandong University of Science and Technology
Abstract:In order to explore the law of surface subsidence caused by mining under heavy conglomerate, combined with the theory of key strata, the internal stability of overburden rock and the causes of deviation of surface subsidence curve are analyzed from three aspects of vertical stress, plastic zone and vertical displacement by numerical simulation method. Based on probability integral method, an improved prediction model of surface subsidence is established. The results show that the initial breaking distance of key strata is 291 m and the periodic breaking distance is 281 m; the cantilever is the main reason for the deviation of subsidence curve; compared with the classical probability integral subsidence prediction model, the relative error of the improved surface subsidence prediction model is reduced by 114%. It is concluded that the improved prediction model of surface subsidence has high accuracy and applicability in the prediction of surface subsidence in mining under thick conglomerate strata.
Keywords:mining  subsidence  massive  conglomerate  numerical  simulation  probability  intergral    improved  model
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