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任务书学院 矿业工程学院 专业年级 采矿工程 学生姓名 任务下达日期：20XX年1月8日毕业设计日期：20XX年3月12日 至 20XX年6月8日毕业设计题目： 城郊煤矿1.5 Mt/a新井设计毕业设计专题题目： 矿井深部巷道围岩变形机理及支护技术的研究毕业设计主要内容和要求：以实习矿井城郊煤矿条件为基础，完成城郊煤矿1.5Mt/a新井设计。主要内容包括：矿井概况、矿井工作制度及设计生产能力、井田开拓、首采区设计、采煤方法、矿井通风系统、矿井运输提升等。结合煤矿生产前沿及矿井设计情况，撰写一篇关于矿井深部巷道围岩变形机理及支护技术的专题论文。完成20XX年国际岩石力学与采矿科学杂志上与采矿有关的科技论文翻译一篇，题目为“Risk assessment of floor water inrush in coal mines based on secondary fuzzy”，论文3763字符。院长签字： 指导教师签字：翻译部分英文原文Risk assessment of oor water inrush in coal mines based on secondary fuzzy Yi Wang*, Weifeng Yang, Ming Li, Xi Liu School of Resources and Geosciences, China University of Mining and Technology, Xuzhou Jiangsu 221116, China Abstract: A secondary fuzzy comprehensive evaluation system is constructed to evaluate the risk of oor water invasion in coal mines. Four rst-grade indices and 13 second-grade indices are determined based on the principles of scienticity, rationality, operability and representative by using fuzzy mathematics theory. Each index is quantitatively graded according to ve risk grades using fuzzy statistical method and expert evaluation method, and the membership degree of every index is constructed. The weight of every index is rationally distributed by analytic hierarchy process (AHP). Evaluations of engineering practice are carried through with hydrogeological data of six mining faces in China. The satisfying evaluation results are consistent with engineering practice.Key words:Floor water inrush；Secondary fuzzy comprehensive evaluation；Analytic hierarchy process (AHP)；Membership degree；Weight1. IntroductionAccording to the latest statistics from Chinese coal mines in 2000 to 2011, it is found that mine water accidents frequently occur, resulting in huge casualties and economic loss (Table 1). These greatly endanger the life of miners and delay the development of coal mine projects. Mining operations in China are often threatened by frequent water inrushes from conned karst aquifers, affecting the safe coal extractions. Evaluating and solving the problem of mining safety has practical signicance in coal extractions. The research of water inrush has been popular in recent years with methods varied. Dumpleton et al. used 3-D visualization and predictive modeling to evaluate the risk of mine water inrush in south Nottinghamshire 1. Hodlur et al. showed a statisticalhydrogeological model to evaluate mining water hazards 2. Zhang presented the water inrush mechanism and applied the nite element method coupled with stress-dependent permeability to predict water inrush, proposing some technical measures to improve mine design and safety 3. Kong used a theory of seepage instability to estimate the harmfulness of water inrush from a coal seam oor in a particular coal mine of the Mining Group, Xuzhou 4. Han et al. presented the methods of strata mechanics and nite element analysis to describe the mechanismofmine water inrush through a fault from the oor 5. Bukowski proposed a risk assessment system to analyze water hazard, based on the factors of inow intensity, the amount of suspended material contained in the water owing into the shaft, the proportion of water-bearing formations in the vertical prole, the condition of the shaft lining and safety pillar, and the history of the shaft 6. Chen applied Fault Tree Analysis (FTA) to analyze the hazard common source of mine water inrush 7. Some of these simplify conditions or factors and fail to profoundly reveal the quantifying relation between water inrush and its affecting indices, while others exist some limitations despite their uniqueness to manage the problem of water inrush. The method of comprehensive evaluation for risk of oor water inrush proposed based on the theory of fuzzy mathematics fully takes into consideration the main indices affecting water inrush with the advantages from qualitative to quantitative and of high accuracy and universal application.2. Design of comprehensive evaluation index systemWith reference to years representative researches on oor water inrush in many countries, the experts abundant research experience and methods 46,821 were used. So based on the scienticity, rationality, operability and representative of the evaluation index system, in this paper four rst-grade indices and 13 second-grade indices are put forward through the analytic hierarchy process (AHP). The evaluation index system of oor water inrush risk consists of four parts on the whole, include: (1) The geological structure index is the primary index controlling the occurrence of water inrush, including fault density, fault water transmitting ability, fracture development degree. (2) The hydrogeology index reects hydrogeology conditions of the aquifer and its relative position to runoff, including conned water pressure, aquifer water yield property, karst development degree and the strong water source recharge. (3) The oor aquifuge index is the only index to obstruct the occurrence of water inrush, including aquifuge thickness, strength and integrity; (4) The mining size index is an induction to water inrush, including mining thickness, mining depth and inclined length of the mining face.The evaluation index system of water inrush risk from coal oor is as follows (Fig. 1). The index set is divided into two grades by us: the rst-grade index set is:U=(U1, U2, U3, U4), and the second-grade index set is:U1=(V1, V2, V3), U2=(V4, V5, V6, V7), U3=(V8, V9, V10), U4=(V11, V12, V13). Fig.1. Comprehensive evaluation index system for risk of floor water inrush3. Grading of evaluation indices and determination of evaluation gradeAccording to the theory of fuzzy mathematics, the evaluation indices should be graded based on risk degree of oor water inrush. In the paper, the parameters of 220 coalfaces suffering from oor water inrush or threat about 100 mining bureaus in China are statistically analyzed, and many experts with many years of research experience in mine water hazard in China University of Mining and Technology are consulted. Combining with the international research achievements 821, the continuous variable indices which can be quantitatively processed are graded with single factor quantization, while the discrete variables hard to quantitatively process are quantita- tively classied by experts grading method. That is, the risk grades of water inrush are included: very high, high, medium, low, very low with scores 5, 4, 3, 2, 1, respectively. The concrete operation methods of expert evaluation method can refer to 2325. Due to the different physical signicance of various indices and inconsistent dimension, the raw data need to be processed and all levels of the boundaries are standardized before fuzzy calculation to ensure that all indices are equivalent and the same sequence. Showed in Table 1, where numbers in the parentheses is the standardized value of single index Table 2.Fig.2. Each indexs curves of membership functions4. Derivation of membership functions of indicesThere are many forms of membership functions. The most commonly used form of membership function includes: normal type, partial large-scale, partial small, triangular fuzzy numbers, lower semi-trapezoidal, trapezoidal and ridge et al. 22,23. Since the form of a variety of membership function is different, but the nal analysis conclusions are consistent. So select what kind of membership function to the study conclusion is not affected. In this paper triangular fuzzy number membership function is chosen.Based on rst-grade geological structure U1, corresponding to every grade very high (A), high (B), medium (C), low (D), very low(E), the membership functions of second-grade index fault density V1 in evaluation risk of oor water inrush are derived as follows:Membership functions of other indices affecting the oor water inrush risk can be constructed with the reference 22,23 and the membership function construction process of second-grade index fault density V1.In order to clearly discover the tendency and change law of membership degree of each index, the curves of membership functions are drawn based on standardized quantitative classication results (see Fig. 2).5. Weight calculation by AHPAHP is a decision analysis method combining qualitative with quantitative analysis, an effective way to determine the weight. The importance order of all indices is determined by making clear the vague concept 22.Judgment matrices of each rst-grade and second-grade indices can be constructed as follows (Tables 37) by referring to 22,25. Maximum eigenvalue of judgment matrix is calculated by sum method 22, a simple method to calculate the corresponding eigenvector. Eigenvector of the largest eigenvalue is the evaluation weights. The judgment matrix maximum eigenvalue lmax1 of second-grade indices (include the fault density V1, the fault water transmitting ability V2, the fracture development degree V3) based on rst-grade geological structure index U1 is 3.1. The judgment matrix maximum eigenvalue lmax2 of second-grade indices (include conned water pressure V4, aquifer wate yield property V5, karst development degree V6, the strong water source recharge V7) based on rst-grade hydrogeology index U is 4.1. The judgment matrix maximum eigenvalue lmax3 of second-grade indices (include aquifuge thickness V8, aquifuge strength V9, aquifuge integrity V10) based on rst-grade oor aquifuge index U3 is 3.1. The judgment matrix maximum eigenvalue lmax4 of second-grade indices (include mining thickness V11, mining depth V12, inclined length of the mining face V13) based on rst-grade mining size index U4 is 3.0. The judgment matrix maximum eigenvalue lmax1 of rst-grade indices (include geological structure index U1, hydrogeology index U2, oor aquifuge index U3, mining size index U4) is 4.1.6. ConclusionsSince analysis of oor water inrush is subject to randomicity and vagueness, two or even multivariate fuzzy evaluation model is constructed based on fuzzy comprehensive evaluation and analytic hierarchy process (AHP) with the theory of fuzzy mathe-matics introduced, comprehensively considering various indices and their relationship simply and fast, accurately and practicably, with evaluation results reliable after actual engineering test.Indices affecting the risk of oor water inrush are so many that different working faces in different coal mines possess different inuential indices. In actual mining process, not only indices above are considered but also increasing or decreasing indices according to different hydrogeology conditions.Interaction of indices should be taken into consideration in actual evaluation because grading of single index is of certain vagueness with fuzzy mathematics method statistical.It being exible, construction of index membership functions can be done referring methods mentioned above or 22,23to choose other methods.References1 Dumpleton S, Robins NS, Walker JA, Merrin PD. Mine water rebound in south Nottinghamshire: risk evaluation using 3-D visualization and predictive modeling. Q J Eng Geol Hydrogeol 2001;34(3):30719.2 Hodlur G, Prakash RM, Deshmukh S, Singh V. Role of some salient geophysical, geochemical, and hydrogeological parameters in the exploration of fresh groundwater in a brackish terrain. Environ Geol 2002;41(7):8616.3 Zhang Jincai. Investigations of water inrushes from aquifers under coalseams. Int J Rock Mech Min Sci 2005;42:35060.4 Kong HL, Miao XX, Wang LZ, et al. Analysis of the harmfulness of water-inrush from coal seam oor based on seepage instability theory. J China Univ Min Tech 2007;17(4):4538.5 Han J, Shi LQ, Yu XG, et al. Mechanism of mine water-inrush through a fault from the oor. Min Sci Tech 2009;19:27681.6 Bukowski P. Water hazard assessment in active shafts in upper Silesian coal basin mines. Mine Water Environ 2011;30:30211.7 Chen JM, Yang RS. Analysis of mine water inrush accident based on FTA. Proc Environ Sci 2011;11:15504.8 Tang JH, Bai HB, Yao BH, et al. Theoretical analysis on water-inrush mechanism of concealed collapse pillars in oor. Min Sci Tech (China) 2011;21:5760.9 Wei JC, Li ZJ, Shi LQ, et al. Comprehensive evaluation of water-inrush risk from coal oors. Min Sci Tech (China) 2010;20:1215.10 Jin DW, Zheng G, Liu ZB, et al. Real-time monitoring and early warning techniques of water inrush through coal oor. Proc Earth Planet Sci 2011;3:3746.11 Sun Jian, Wang Lianguo, Wang Zhansheng, et al. Determining areas in an inclined coal seam oor prone to water-inrush by micro-seismic monitoring. Min Sci Tech (China) 2011;21:1658.12 Hua X, Zhang WQ, Jiao DZ. Assessment method of water-inrush risk induced by fault activation and its application research. Proc Eng 2011;26:4418.13 Wu JW, Jiang ZQ, Zhai XR. Research on controlling of rock mass structure on water inrush from coal seam oor in Huaibei Mining Area. Proc Eng 2011;26:34350.中文译文根据二级模糊对煤矿底板突水风险进行评估王毅*，杨伟峰，李明，刘曦中国矿业大学，资源与地球科学学院，中国，江苏，徐州221116摘要：构建一个二级模糊综合评估体系对煤矿底板突水风险进行评估。根据科学性，合理性，可操作性和代表性的原则利用模糊数学理论对四个一级指标和十三个二级指标进行检测。根据五个风险等级，采用模糊统计法和专家评估方法对各项指标进行量化分级，并构建各指标的隶属度。通过层次分析法（AHP）对各指标的权重进行合理分布。利用中国6个煤矿回采工作面的水文地质资料对工程的实际效果进行评估。评估结果的满意度与工程实践效果一致。关键词：底板突水;二级模糊综合评估;层次分析法（AHP）;隶属度;权重1简介据最新统计，从2000年到2011年中国煤矿透水事故频繁发生，造成巨大的人员伤亡和经济损失（见表1）。这极大的危及到矿工的生命和影响煤矿项目的发展。在中国，采矿作业往往受到来自承压岩溶含水层入侵的威胁，影响煤矿的安全开采。评估和解决这个问题对煤矿开采具有现实意义。近年来人们采用各种方法对煤矿突水进行研究。（1）Dumpleton等人使用3-D可视化和预测建模的方法对诺丁汉南部的煤矿突水风险进行评估。（2）hodlur等人利用数字水文地质模型对开采水害进行评估。（3）张提出了突水机理并应用有限元结合压力渗透率的方法去预测突水危害，而且提出了一些技术措施，以改进矿井设计和安全性。（4）孔使用防渗不稳定的理论对徐州矿业集团的一个煤层底板突水危害进行评估。（5）韩等人通过一个来自煤层底板的故障提出了利用地层力学和有限元分析的方法来描述突水机理。（6）Bukowski根据水流入侵的强度因素，流入竖井井筒中的悬浮物的数量，在垂直剖面上的含水层的比例，井筒内部的支护条件以及井筒的使用年限，提出了一个风险评估系统来分析水害。（7）陈应用故障树分析（FTA）方法来分析煤矿突水的危险源。虽然这是一种独特的治理突水问题的方法，但仍然存在一些局限性，一些简化后的条件或者因素不能深刻揭示突水问题和它的影响因素之间的量化关系。因此利用从定性到定量和高准确性及普及应用的优势，基于模糊数学理论的底板突水风险综合评估方法充分考虑了影响突水问题的主要指标。2. 综合评估指标体系的设计近年来，不同国家的许多专家进行了有关底板突水方面的很多具有代表性的研究，我们充分参考了这些专家的丰富经验和方法46,821。因此在评估指标体系的科学性，合理性，可操作性以及代表性的基础上，本文通过层次分析法（AHP）提出了四个一级指标和十三个二级指标。整体而言，底板突水风险评估指标体系包括四部分：（1）地质构造是影响突水发生的主要指标，包括断层密度，水渗透断层的能力，裂隙发育程度。（2）水文地质指标反映了含水层的水文地质条件和径流的相对位置，包括承压水压力，含水层的水量属性，岩溶发育程度和充足的水源补给。（3）底板隔水性是唯一一个阻止突水发生的指标，包括底板隔水层的厚度，隔水层的强度和完整性。（4）矿井开采规模也是一个导致突水的指标，包括综采工作面的开采高度，深度以及倾斜长度。煤层底板突水风险评估指标体系如下（图1）。指标分为两个等级：其中一级指标为：U=(U1, U2, U3, U4)，二级指标为U1=(V1, V2, V3), U2=(V4, V5, V6, V7), U3=(V8, V9, V10), U4=(V11, V12, V13).图1 底板突水风险的综合评估指标体系3. 评估指标的分级和评估等级的确定根据模糊数学理论，评估指标应根据底板突水危险程度进行分级。在本文中，统计分析了中国遭受突水的220个回采工作面或者是100个遭受水害的矿务局的数据情况，并征询了中国矿业大学多位具有多年研究水害经验的专家。结合国际上的研究成果8-21，能够量化处理的的连续变量指标利用单因子进行量化分级，同时不能量化处理的离散变量通过专业的分级方法进行定量分类。也就是说，突水风险等级分为很高，高，中，低，很低，对应分数分别为5, 4, 3 ,2, 1。专家评估的具体操作方法可以参考23-25。由于各项指标具有不同的物理意义和不一致性，因此在模糊计算之前需要处理原始数据，并且边界的各方面都要标准化处理，以保证各项指数具有相同的意义，相同的序列。显示在表一括号中的数字是表二单一指标的标准化值。 表1 2000年到2011年煤矿透水事故 年份 事故数量 死亡人数 表2 突水风险指标等级评估指标 突水风险等级 很高 高 中等 低 很低1.极低等级的隶属函数 2.低等级的隶属函数 3.中等级的隶属函数 4.高等级的隶属函数5.极高等级的隶属函数图2 各个指标的隶属函数曲线4. 各指标隶属函数的推导隶属函数有多种形式。最常用的隶属函数形式包括：正常型，偏大型，偏小型，模糊三角型，半拱形，拱形等22,23。虽然各种隶属函数的形式不同，但最终的分析结论是一致的。因此选择什么样的隶属函数对最终的结论是没有影响的。所以本文选取模糊三角函数作为隶属函数。根据一级地质构造指标U1对应的每一级 很高（A），高（B），中等（C），低（D），很低（E），评估底板突水危害的二级指标断层密度V1的隶属函数的推导过程如下：影响底板突水危害的其他指标的隶属函数可以参考22,23和二级指标断层密度V1的隶属函数构造过程进行构建。为了清楚的发现各指标隶属程度的变化规律和趋势，隶属函数曲线应该在标准量化分类结果的基础上进行绘制（见图2）。5. 通过层次分析法进行权重计算AHP是一种定性和定量分析相结合的决策分析方法，是一种有效的决策权重的方法。通过清除模糊的概念22从而确定各指标的重要程度。每个一级指标和二级指标的判断矩阵构造如下（表3-7）22,25。判断矩阵的最大特征值通过求和法进行计算，它是一种简单的计算相应特征向量的方法。最大特征值的特征向量是评估权重值。建立在一级地