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基于人工神经网络的巷道围岩分类与支护参数优化研究
The Study Based on Artificial Neural Network of Classification of Surrounding Rock and Optimization of Supporting Parameters
【作者】 邓福康;
【导师】 汪仁和;
【作者基本信息】 安徽理工大学 , 岩土工程, 2009, 硕士
【摘要】 随着煤矿开采深度的增加,采掘工作将在高地应力、围岩条件很差的环境中进行,导致巷道被整体压垮、底板膨胀鼓起、拱顶下沉、两帮收敛、鞍形大变形破坏等,这对巷道支护技术提出了更高的要求。课题以皖北煤电集团钱营孜煤矿岩石巷道为背景,开展矿井岩石巷道围岩分类与支护方案优化技术研究,具有重要的现实意义。影响煤矿巷道围岩稳定性分类的因素很多,主要有自然因素与开采技术因素。作者结合两淮地区煤系地层特点,选取BQ分类方法作为围岩分类样本空间依据。论文基于BP神经网络原理,利用MATLAB神经网络工具箱函数,在收集了大量淮南、皖北矿区的工程地质条件、巷道围岩情况以及相应的支护参数的基础上,编制深井巷道围岩分类与支护优化设计系统软件。深井巷道围岩分类与支护优化设计系统软件主要包括两方面功能,其一,实现煤矿巷道围岩稳定性分类;其二,实现煤矿巷道支护参数优化设计。在钱营孜煤矿西翼回风与3212巷道应用此软件的设计结果,经过现场锚杆受力、深部位移及松动圈监测,表明此软件的设计结果合理。该软件的成功应用,可明显缩短支护设计时间,降低巷道失稳率与返修率,且软件界面人性化,易于操作,所需输入的资料,大部分为下拉式选择方式,易于有针对性的收集,具有一定的应用价值。
【Abstract】 The job will work at highland stress and very poor rock conditions environment with the increase of mining depth, which may lead to the whole tunnel crush, floor heave, dome subsidence, two sidewalls convergence, large deformation damage like saddle-shaped and so on, all of these require new technique in tunnel supporting. This thesis takes the roadway supporting of QianYingzi coal mine in WanBei coal group as the engineering background, the study of rock classification of deep well and optimized design of supporting were carried out, it have important Practical significance.Many factors influence the stability of surrounding rock classification, mainly include natural and exploitation technical these two factors. Considered the characteristics of coal measure strata. The author select BQ classification as a basis for the samples of classification of surrounding rock. The software system of rock classification of deep well and optimized design of supporting was developped, which based on BP neural network and used MATLAB neural network toolbox function, The basis of collecting much engineering geological information HuaiNan, HuaiBei mining area, as well as the corresponding support parameters.The software system mainly consists of two functions. First, it can achieve classification of stability of surrounding rock; Second, it can implmentation realize optimization and decision-making of supporting parameter in mine roadway. At the XiYi return aircourse and the 3212 tunnel of QianYingzi coal mine, the outcome of the decision-making of application of the software design, after the scene by the bolt force, and deep displacement and broken zone monitoring, it shows that this software is reasonable to decision-making. The software in the successful application of the QianYingzi coal mine, can obviously shorten the design supporting time, reduce the percentage of products sent back for repair and destabilization, The software is humanized, easy to operate, and the required information, most of the information were inputed by the drop-down selection; we can see that the software have some application value.
【Key words】 rock classification; supporting design; BP neural network; MATLAB;