巷道围岩稳定性分类与支护决策智能研究

发布时间：2018-06-22 10:47

本文选题：围岩稳定性分类 + 支护决策　；参考：《安徽理工大学》2015年硕士论文

【摘要】：随着我国经济的发展,能源的需求量不断上升,煤炭的消耗也越来越大,煤矿开采向深部延伸。随着开采深度的增加,巷道围岩的支护难度加大,这对支护技术提出了新的要求。锚杆支护是目前煤矿岩石巷道的主要支护方式之一,现有支护决策系统存在不足,要做改进。本文主要以两淮矿区岩石巷道为工程背景,收集了三十多条巷道工程地质、水文地质情况,以及支护方案的数据,对样本数据初步分类处理,选择了其中三十条作为决策系统训练样本,其余三条作为验证样本。同时,借助MATLAB软件开发了适合两淮矿区的“深井巷道围岩稳定性分类及支护决策系统”。通过决策系统的开发研究过程,得到以下几点结论： 1、得出了巷道变形失稳以及支护的影响因素,和相应支护设计指标的选取原则。 2、基于工程岩体分级理论和专家评分法原则,对样本巷道围岩进行了分类,为决策系统提供了训练样本。 3、基于人工神经网络预测模型,构建了决策系统围岩分类与支护网络模型；此网络输入层神经节点数为8,隐含层为1层,隐含层节点数为13,输出层节点数为16。 4、基于MATLAB软件开发出了适应两淮矿区不同埋深岩石巷道的围岩分类与支护决策系统,利用GUI界面设计了三大系统功能模块：(1)神经网络模型的建立与选择模块；(2)巷道围岩稳定性分类模块；(3)巷道支护设计模块。 5、选择了II7226N底抽巷作为试验巷道,分别用FLAC3D软件数值模拟和现场巷道变形观测的手段对决策系统进行验证,试验结果表明本支护决策系统具有较强的实用性和可靠性,可以为巷道支护施工提供依据。
[Abstract]:With the development of economy in China, the energy demand is rising and the consumption of coal is increasing. With the increase of mining depth, it is more difficult to support the surrounding rock of roadway, which puts forward new requirements for supporting technology. Bolt support is one of the main supporting methods of rock roadway in coal mine at present. The existing supporting decision system is deficient and should be improved. In this paper, taking the rock roadway of Lianghuai mining area as the engineering background, more than 30 tunnel engineering geology, hydrogeological conditions and supporting scheme data are collected, and the sample data are preliminarily classified and processed. Thirty of them were selected as training samples for decision-making system and the other three as validation samples. At the same time, with the help of MATLAB software, a "classification and support decision system of surrounding rock stability of deep roadway" is developed for Lianghuai mining area. Through the development and research process of the decision-making system, the following conclusions are obtained: 1, the deformation and instability of roadway and the influencing factors of support are obtained. Secondly, based on the classification theory of engineering rock mass and the principle of expert scoring method, the rock surrounding rock of the sample roadway is classified. A training sample is provided for the decision making system. 3. Based on the artificial neural network prediction model, the wall rock classification and support network model of the decision system is constructed, the number of ganglion points in the input layer of the network is 8, and the hidden layer is one layer. The number of hidden layer nodes is 13 and the output layer nodes are 16.4. Based on MATLAB software, a wall rock classification and support decision system for different buried rock roadways in Lianghuai mining area is developed. Using GUI interface, three system function modules are designed: (1) neural network model establishment and selection module; (2) roadway surrounding rock stability classification module; (3) roadway support design module. 5, select II7226N bottom roadway as test roadway, The numerical simulation of FLAC3D software and the field observation of roadway deformation were used to verify the decision system. The experimental results show that the supporting decision system has strong practicability and reliability, and can provide the basis for roadway support construction.
【学位授予单位】：安徽理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TD353

【参考文献】