广义时间函数参数求取方法研究

基于区间估计算法求取Knothe时间函数参数的原理,结合由开采引起地表沉陷的一般事实规律提出了采用区间估算法求取广义时间函数参数的构想,并建立了相应的数学模型,给出了地表移动动态预计的基本原理.结合实例,将采用该模型求得的参数进行地表动态沉陷预计的结果与实测资料进行对比分析.结果表明,该模型对矿区地表下沉动态预计能够达到较好的效果,说明该模型具有一定的实用价值.     

开采沉陷动态参数预计的三次指数平滑法

在我国,概率积分法是应用最多的一种开采沉陷预计方法．该方法中的预计参数在不同采动程度下是变化的．能否准确获知动态参数的变化规律将决定着开采沉陷的预计精度．为较好地解决这一问题,引入了一种动态参数预计的新方法,即三次指数平滑法．应用此方法对实测资料进行了预计和比较,结果表明,各预计参数的平均相对误差都小于4％．由此可见,该方法对开采沉陷的预测具有一定的应用价值．     

倾斜煤层开采沉陷预计模型的构建

概率积分法在我国煤矿开采地表沉陷预计领域得到了广泛应用,该方法具有对称分布特征,在水平煤层开采沉陷预计时精度最好,而倾斜煤层开采后地表下沉盆地的形态往往呈现偏态性,采用该方法对其进行预计偏差较大。针对此问题,建立了工作面随体坐标系和地表直角坐标系,利用随机介质理论在工作面随体坐标系中实现单元开采沉陷影响的叠加积分运算,然后将工作面随体坐标系中地表某点的沉陷影响转换到地表直角坐标系中,最终构建地表直角坐标系下的倾斜煤层开采沉陷偏态预计模型。分析认为,倾斜煤层单元开采引起的地表下沉符合威布尔偏态分布规律,结合实例对预计模型的有效性进行了验证。该模型预计精度随煤层倾角的增加而逐渐降低,研究结果可为相似矿区的沉陷预计提供理论和技术支持。     

西山矿区地表移动观测资料综合分析

本文为《西山矿区开采沉陷规律与沉陷环境治理》研究成果的一部分,通过大量观测资料的分析,求取了相应于西山矿区地形、地质与开采条件的地表移动角量参数。动态参数及预计参数,这些参数对类似条件的矿区亦有参考价值。     

开采沉陷动态参数预计的三次指数平滑法

在我国,概率积分法是应用最多的一种开采沉陷预计方法.该方法中的预计参数在不同采动程度下是变化的.能否准确获知动态参数的变化规律将决定着开采沉陷的预计精度.为较好地解决这一问题,引入了一种动态参数预计的新方法,即三次指数平滑法.应用此方法对实测资料进行了预计和比较,结果表明,各预计参数的平均相对误差都小于4%.由此可见,该方法对开采沉陷的预测具有一定的应用价值.     

开采沉陷空间问题参数分析方法及其应用研究

本文以概率积分法沉陷模型为例，对矩形及任意形状工作面开采时地表沉陷空间问题参数分析方法进行了研究，并开发了相应的软件。在此基础上，对该分析方法在处理不规范观测站资料中的应用及在开采沉陷模型识别中的应用用实例作了介绍。实例证明，该分析方法及计算软件是正确的，且使用方便，可在矿山开采沉陷数据处理中推广应用。     

人工神经网络在煤矿开采沉陷预计中的应用研究

提出了利用人工神经网络技术进行开采沉陷定量预测的新方法 .研究了影响因素的选取、开采沉陷预计模型的建立以及模型的应用等问题 .采用 BP神经网络算法对开采沉陷量进行了建模和预测 .结果表明 ,用神经网络模型对复杂的开采沉陷系统进行模拟预测 ,具有理论上的可行性和现实意义 ,说明人工神经网络技术在开采沉陷预计领域中具有实用价值     

条带开采的三维层状介质理论及其应用研究

条带开采是“三下”（建筑物下、水体下、铁路下）压煤开采中,控制地表沉陷的一种比较有效的方法．然而,现有的条带开采尺寸设计一般还是采用威尔逊法,地表沉陷预计沿用全采的该率积分法由于预计参数采用经验公式确定,而对条带开采的主控因素未能明确给出或系统考虑,其预计误差相对较大．此外,对条带开采岩体的内部移动机理知之甚少,如上覆岩体（或下伏岩体）的波浪移动带的定义及其高度的计算,等等．总之,条带开采未能形成系统的理论,从而大大影响了条带开采地表沉陷的预计精度,这就限制了条带开采的合理使用．为此,提出了“条…     

开采沉陷预计参数的确定方法

基于相似理论的基本原理 ,根据我国 1 2 0多个地表移动观测站的监测数据 ,导出了开采沉陷预计参数与岩体综合变形模量E、采深H和采厚M等因素之间的统计公式 ,即地表下沉系数 q、水平移动系数b、主要影响角正切tanβ和拐点偏移距与采深的比值S0 /H与相似准则E/Em 和 ρH2 ( 1 0 0MEm)之间呈线性关系 .研究表明 :岩体的综合变形模量对开采沉陷预计参数的影响显著 ,采深主要影响拐点偏移距 ,对其它预计参数的影响甚微 ,而采厚对所有预计参数的影响都很小 ,可忽略     

开采沉陷预计参数的确定方法

基于相似理论的基本原理,根据我国120多个地表移动观测站的监测数据,导出了开采沉陷预计参数与岩体综合变形模量E、采深H和采厚M等因素之间的统计公式,即地表下沉系数q、水平移动系数b、主要影响角正切tanβ和拐点偏移距与采深的比值S0/H与相似准则E/Em和ρH2(100-MEm)之间呈线性关系.研究表明岩体的综合变形模量对开采沉陷预计参数的影响显著,采深主要影响拐点偏移距,对其它预计参数的影响甚微,而采厚对所有预计参数的影响都很小,可忽略.     

Subsidence control method by inversely-inclined slicing and upward mining for ultra-thick steep seams

Ultra-thick steep coal seam mining will inevitably lead to the increase of greater and violent ground subsidence and deformation. A subsidence control method by inversely-inclined slicing and upward mining is proposed in this paper. By this method, the sequence of collapse of overlying strata and the direction of propagation of strata movement are changed, the extent of roof-side deformation thereby is lessened, and boundary angle of roof-side subsidence is reduced by 5°–10°. The mechanism of this mining method for control of strata movement has been evidenced by numerical simulation and experiments with similarity materials. A subsidence prediction model based on the variation of mining influence propagation angle can be used to evaluate the surface movement and deformation of the mining method. The application of the method in No.3 Mine in Yaojie mining area has yielded the expected result.     

Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer

The dynamic ground subsidence due to underground mining is a complicated time-dependent and ratedependent process. Based on the theory of rock rheology and probability integral method, this study developed the superposition model for the prediction and analysis of the ground dynamic subsidence in mining area of thick loose layer. The model consists of two parts(the prediction of overlying bedrock and the prediction of thick loose layer). The overlying bedrock is regarded as visco-elastic beam, of which the dynamic subsidence is predicted by the Kelvin visco-elastic rheological model. The thick loose layer is regarded as random medium and the ground dynamic subsidence is predicted by the probability integral model. At last, the two prediction models are vertically stacked in the same coordinate system, and the bedrock dynamic subsidence is regarded as a variable mining thickness input into the prediction model of ground dynamic subsidence. The prediction results obtained were compared with actual movement and deformation data from Zhao I and Zhao II mine, central China. The agreement of the prediction results with the field measurements show that the superposition model(SM) is more satisfactory and the formulae obtained are more effective than the classical single probability integral model(SPIM), and thus can be effectively used for predicting the ground dynamic subsidence in mining area of thick loose layer.     

An influence function method based subsidence prediction program for longwall mining operations in inclined coal seams

The distribution of the final surface subsidence basin induced by longwall operations in inclined coal seam could be significantly different from that in flat coal seam and demands special prediction methods. Though many empirical prediction methods have been developed, these methods are inflexible for varying geological and mining conditions. An influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, significant modifications have been made to the original Knothe function to produce an asymmetrical influence function. The empirical equations for final subsidence parameters derived from US subsidence data and Chinese empirical values have been incorporated into the mathematical models to improve the prediction accuracy. A corresponding computer program is developed. A number of subsidence cases for longwall mining operations in coal seams with varying inclination angles have been used to demonstrate the applicability of the developed subsidence prediction model.     

SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed. Based on least squares support vector machine (LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction. Many of the geological and mining factors involved are related in a nonlinear way. The new model is based on statistical theory (SLT) and empirical risk minimization (ERM) principles. Typical data collected from observation stations were used for the learning and training samples. The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network (BPNN) model. The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model. The LS-SVM model was faster in computation and had better generalized performance. It provides a highly effective method for calculating the predicting parameters of the probability-integral method.     

基于遗传规划的采空区地面沉陷预测分析

针对地下开采引起的岩层移动与变形,提出一种基于遗传规划的采空区地面沉陷预测新方法.基于MTLAB工具编制的遗传规划程序,选取影响地面沉陷的主要因素,搜集学习样本对程序进行了训练,建立了采空区地面沉陷预测的遗传规划模型.最后,利用有关实测数据,对模型进行了实例检验.结果表明,预测误差在工程允许范围之内,应用遗传规划方法进...     

厚松散层下开采预计的概率积分法修正模型

由于厚松散层下开采具有明显不同于一般地质条件下的沉陷特性,结果导致在用传统的概率积分法对其预计时,预计结果与实际往往偏差较大.基于厚松散层土体与基岩岩性不同,根据岩层移动分层传递的观点,结合厚松散层下开采地表移动特征,建立了厚松散层下开采地表移动预计的概率积分法修正模型,对厚松散层下开采地表移动预计及建筑物保护有一定指导意义.     

A new approach to predicting mining induced surface subsidence

1 INTRODUCTIONMining induced surface subsidence often re-sults in various kinds of damages to the structuresandinfrastructures in the subsidence area[1 4]. Thepipes will be broken and fractured,the buildingswill be caused to tilt or collapse and the roadfoun-dation and acequia will be damaged because of thesubsidence . Especially in the case of open stopemining under hard rock formation,this subsidencewill suddenly occurr . For example , at about 11p.m.on December 27 ,1999 ,large scale of…