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1.
近松散层开采覆岩导水裂隙带沟通上覆含水层导致了顶板水害事故的发生。在其他开采因素相似时,工作面顶板覆岩结构的不同会致使导水裂隙带发育高度出现较大差异。为此,通过收集淮北煤田17例近松散层开采覆岩导水裂隙带发育高度实测数据作为训练样本,利用一行两列向量对近松散层工作面顶板覆岩结构进行量化,并联合煤层采厚、煤层倾角、工作面斜长、开采深度、松散层厚度共计6个影响因素作为输入数据,实测导水裂隙带发育高度作为输出数据,依据径向基函数神经网络建立了考虑覆岩结构影响的近松散层开采导水裂隙带发育高度预测模型。并将该预测模型应用于淮北煤田中的青东煤矿,经钻孔冲洗液漏失量与钻孔彩色电视观测验证,获得预测结果相对误差为3.3%,低于《“三下”开采规范》中经验公式计算误差19.2%。该方法为近松散层开采导水裂隙带发育高度的合理确定提供了理论支持。 相似文献
2.
煤矿的开采利用给国民经济发展带来巨大的收益,但也引发了许多环境地质问题,特别在煤层开采过程中,煤层上覆基岩变形破坏形成的裂隙通道极易发生矿井涌（突）水事故,时刻威胁着井下工人的生命安全。本文以灵新煤矿051505工作面为研究对象,利用Flac3D数值模拟软件,对14号主采煤层上覆基岩导水裂隙带高度进行了模拟研究。模拟结果表明：当煤层开采厚度为2.5 m时,导水裂隙带发育最大高度为59.5 m。同时选取经验公式法对导水裂隙带高度进行了计算。最终通过钻孔实测法得到的结果与前两种方法对比分析,数值模拟结果与钻孔实测结果基本吻合,认为数值模拟方法能够高效、简单、合理达到预测导水裂隙带高度的目的,也为同类矿井安全、绿色生产提供一定的借鉴。 相似文献
3.
为准确预测非充分采动导水裂缝带高度,选取开采厚度M、煤层埋深H、工作面倾斜长度L、煤层倾角α、覆岩力学性质R、覆岩结构特征S为非充分采动导水裂缝带高度主要影响因素。采用量纲分析建立了导水裂缝带高度与M,H,L,α,S间的无量纲关系式。结合30组实测数据,采用多元回归得到无量纲关系式的最优函数关系式。选取2个非充分采动工作面导水裂缝带现场实例对预测模型进行了工程验证,预测模型预测结果与实测结果吻合良好,其相对误差分别为3.64%和2.93%,预测模型的预测精度可以满足煤矿安全生产现场需要。 相似文献
4.
综采一次采全高顶板导水裂缝带发育高度的计算公式及适用性分析 总被引：2，自引：0，他引：2
确定煤层顶板导水裂缝带高度可为顶板防治水、采掘工程布置、防水煤柱留设以及瓦斯抽采设计提供依据。采用井下仰孔注水测渗漏法,实测山西西山煤田镇城底矿8煤导水裂缝带高度为57.98 m,其中冒落带高度16.72 m,裂隙带高度41.26 m。依据实测结果并收集了8个矿综采一次采全高中硬覆岩下导水裂缝带高度数据,利用数理统计回归分析的方法,得出了适用于综采一次采全高中硬覆岩下导水裂缝带高度计算的经验公式,并与《煤矿安全规程》中相应经验公式进行对比分析,结果表明,该公式适用性好,而《煤矿安全规程》中有关公式应用于中厚煤层综采一次采全高开采条件预测,其误差较大。 相似文献
5.
导水裂隙带高度的确定对松散承压含水层下煤矿安全开采和矿区生态环境保护具有重要意义。以往根据塑性区判断导水裂隙带范围的数值模拟方法不能完全反映覆岩的破断机制。为了更准确地预测导水裂隙带发育高度，应用断裂力学方法，将裂纹尖端K场区内的应力强度因子断裂判据与摩尔-库伦屈服准则结合，提出了原生裂隙存在时的岩石断裂准则。利用自仿射分形模型建立起原生裂隙场分布，并通过有限元分析软件COMSOL Multiphysics将原生裂隙场和岩石断裂准则应用到导水裂隙带发育的数值模拟中，对淮北煤田青东煤矿的839工作面开采进行了模拟计算。结果显示，考虑原生裂隙时，导水裂隙带在贯通后高度达到92.5 m。与传统数值模拟和经验公式法相比，考虑原生裂隙的模拟结果与现场测量结果更为接近。这说明，采用自仿射分形模型所生成的裂隙场可以较好地模拟岩体内复杂而无序的原生裂隙分布，且与传统数值模拟和经验公式法相比，考虑原生裂隙的模拟方法能够更好地反映导水裂隙带的发育规律。 相似文献
6.
针对煤层底板突水预测问题,在总结现有突水预测方法和理论的基础上,通过特征选择实验得出水压、距工作面距离、砂岩段厚度、煤层厚度、煤层倾角、断层落差、裂隙带、开采面积、采高、走向长度是影响突水发生的主要因素,这些因素具有复杂、非线性的特点。提出基于长短时记忆（LSTM）神经网络构建的突水预测模型,将煤矿突水实例的数据作为样本数据对模型进行训练。最后,将LSTM神经网络模型与遗传算法-反向传播（GA-BP）神经网络模型和反向传播（BP）神经网络模型进行对比实验。实验结果表明,LSTM神经网络模型在测试集上的预测正确率更高,稳定性更好,更适用于煤层底板突水预测。 相似文献
7.
基于BP神经网络的泥石流平均流速预测 总被引：4，自引：0，他引：4
泥石流平均流速是泥石流防治工程中不可缺少的重要参数,准确地预测泥石流平均流速对于泥石流防治工程的设计是至关重要的。将BP神经网络应用于泥石流平均流速的预测:将泥石流平均流速的影响因素--泥沙平均粒径、泥深、沟床比降和泥石流密度作为BP神经网络的输入单元,通过对云南东川蒋家沟泥石流观测数据的训练与预测建立了泥石流平均流速的BP神经网络预测模型。将预测结果与东川公式和曼宁修正公式的计算结果进行对比:曼宁修正公式和东川公式预测结果最大误差分别为27%和7.3%,BP神经网络的预测结果最大误差仅为3.2%,BP神经网络的预测精度是最高的,可见此方法对泥石流平均流速预测具有适用性和准确性。最后应用此方法预测了乌东德水电站近坝库区内的3条泥石流的平均流速分别为12.8 m/s、11.3 m/s和13.0 m/s,为库区泥石流防治工程提供了可靠的参考数据。 相似文献
8.
覆岩分类是经验类比法预测导水裂隙带高度的基础,应用模糊数学m相分类法对煤层顶板物理力学性质指标进行处理,寻求煤层顶板对岩性"软""硬"的隶属函数,建立模糊关系矩阵,进行煤层顶板类别的综合判别,并将其结果用于确定计算导水裂隙带高度经验公式的系数值。这一方法可以在很大程度上消除人为因素对覆岩分类的影响,使预测的导水裂隙带高度更符合实际情况。 相似文献
9.
采用相似模型试验和数值模拟相结合的方式,分析了奥陶系石灰岩推覆体含水层下煤层开采的覆岩破坏及地表沉陷 特征。经验公式计算,相似模型试验和数值模拟的对比分析表明：该特殊地质条件下,不考虑渗流场影响时,垮落带高度 为采厚的2.2~4.5倍,导水裂隙带高度为采厚的14.0~19.1倍;渗流场的存在使导水裂隙带及垮落带高度增加,此时垮采比为 4.8,裂采比为20.6。因此,从偏安全的角度考虑,煤层的实际开采过程应考虑渗流场的影响,以渗流场-应力场耦合作用 下的导水裂隙带高度作为安全煤（岩）柱合理留设的依据。 相似文献
10.
工作面回采过程中,覆岩破坏特征对于煤矿水灾害和瓦斯防治具有重要意义,为了进一步研究综放开采覆岩破坏特征。以山西某矿5.82m大采高工作面为试验面,采用分段注水、钻孔电视、地质雷达、微震监测探测覆岩破坏高度,对破坏过程进行了数值模拟研究,并对裂隙演化进行了相似模拟试验,同时对传统经验公式进行了修正,研究结果表明:综放开采垮落带发育高度为43.1m,断裂带发育高度为86.7m;垮落带、断裂带、导水断裂带各测试方法之间相差分别小于4.5%、7.1%、9.0%;工作面采动前,裂隙发育度低,而采动后,裂隙数量明显增多,发育度增加;近煤壁区域为裂隙聚集区,密度曲线呈“蛇”型分布;得到新的适合该矿地质条件下的覆岩导水断裂带发育高度经验公式。 相似文献
11.
Factures caused by deformation and destruction of bedrocks over coal seams can easily lead to water flooding (inrush) in mines, a threat to safety production. Fractures with high hydraulic conductivity are good watercourses as well as passages for inrush in mines and tunnels. An accurate height prediction of water flowing fractured zones is a key issue in today's mine water prevention and control. The theory of leveraging BP artificial neural network in height prediction of water flowing fractured zones is analysed and app-lied in Qianjiaying Mine as an example in this paper. Per the comparison with traditional calculation results, the BP artificial neural network better reflects the geological condi-tions of the research mine areas and produces more objective, accurate and reasonable results, which can be applied to predict the height of water flowing fractured zones. 相似文献
12.
Factures caused by deformation and destruction of bedrocks over coal seams can easily lead to water flooding(inrush)in mines,a threat to safety production.Fractures with high hydraulic conductivity are good watercourses as well as passages for inrush in mines and tunnels.An accurate height prediction of water flowing fractured zones is a key issue in today's mine water prevention and control.The theory of leveraging BP artificial neural network in height prediction of water flowing fractured zones is analysed and applied in Qianjiaying Mine as an example in this paper.Per the comparison with traditional calculation results,the BP artificial neural network better reflects the geological conditions of the research mine areas and produces more objective,accurate and reasonable results,which can be applied to predict the height of water flowing fractured zones. 相似文献
13.
Zhongchang Wang Chuan Wang Zechuan Wang 《Geotechnical and Geological Engineering》2018,36(5):3019-3028
The water inrush of roof induced by mining was related to the height of water flowing fractured zone under large-scale water bodies. Based on the drilling revealed stratum, the thickness of different overlying layers was obtained within the scope of Santaizi reservoir. The height of water flowing fractured zone of different workface on the outside of reservoir under the condition of fully mechanized level mining area was the prediction sample, the generalized analysis of sensitive factors that affected the development of water flowing fractured zone was carried on. The mining depth, dip angle of coal seam, mudstone ratio, compressive strength, mining thickness and the inclined length of the goaf were selected as the influence factors to predict the height of water flowing fractured zone. The height of water flowing fractured zone of unmined working face within the scope of Santaizi reservoir was obtained by objective entropy method. The index weight value of each influence factor was determined. The thickness of the different overlying rock layers above water flowing fractured zone was obtained. And the safety evaluation of water-inrush of unmined working face within the scope of Santaizi reservoir was studied. The important parameter and technical support were provided for the rational design and mining of the working faces under the reservoir. 相似文献
14.
The movement and failure of overlying strata induced by underground coal mining cause “three zones,” including the caving zone, the water-conductive fractured zone, and the sagging zone from the bottom up. For knowledge about the height of the water-conductive fractured zone, there has been no empirical or theoretical formulae for thick coal seam using fully mechanized longwall mining with sublevel caving. This paper presents a methodology of determining the height of the water-conductive fractured zone based on the radial basis function neural networks (RBFNN) model in MATLAB software. Before modeling, the relationship between the height of the water-conductive fractured zone and mining thickness, the lithologic character of the overburden and its composite structures, and workface parameters was studied. After that, 32 and 7 measured data were used as training and testing samples, respectively. It has been found that the average relative error is 6% and the maximum relative error is 10% for 7 test samples by comparing actual results with predicted results. The model was applied to the no. 31503 workface in the Gaozhuang coal mine for safety evaluation. The predicted value is 59.6 m, and the measured value is 55.9 m. The RBF-based model shows much better performance than empirical formulae in the Regulations for Coal Mining and Coal Pillar Design under Buildings, Water-bodies, Railways and Main Shafts for the prediction of the height of the water-conductive fractured zone for fully mechanized mining with sublevel caving. 相似文献