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中文摘要：
坝基岩体裂隙发育直接影响水电工程的结构安全与渗流稳定,建立准确的坝基岩体三维裂隙网络模型是分析坝体安全的基础。岩体裂隙数量巨大,分布复杂,以往的研究多采用Monte Carlo简单随机抽样方法获取裂隙参数的样本数据,进而建立二维或三维裂隙网络模型,忽略了揭露面以下裂隙数据对模拟结果的影响,计算结果稳定性差、精度低,且容易产生样本坍塌的问题。本文提出了水电工程坝基岩体三维裂隙网络的模拟方法,即在坝基揭露面裂隙数据的基础上,融合钻孔摄像技术获取的钻孔全孔壁资料,对裂隙面位置、大小、数量和产状等参数进行统计;然后采用拉丁超立方抽样（Latin Hypercube Sampling）方法对裂隙参数进行随机模拟;最终利用Visual Geo软件建立三维裂隙网络模型。该方法应用于某水电站坝基裂隙三维网络模拟中,并与传统的Monte Carlo方法进行对比,结果表明：该方法在融合揭露面裂隙资料和钻孔摄像数据的前提下,空间填充性好,模拟结果更加接近实测值,为水电工程坝基稳定分析提供了可靠的技术手段和准确的模型基础。
英文摘要：
Rock mass fractures in dam foundation directly affect structure safety and seepage stability of hydropower projects. Accurate 3-D models of rock mass fracture networks provide a solid basis for analysis of dam safety. In construction of such network models, random simulation methods are convenient in obtaining sample data of fracture parameters from the large quantity and complicated distribution of rock mass fractures, and recent research is focusing on the Monte Carlo method for random sampling. However, such an approach ignores the importance of those fractures lying below the exposed surface layer, and suffers from poor numerical stability, low accuracy, and the problem of sample collapsing. This paper presents a new simulation method of 3-D fracture networks in dam foundation using Latin hypercube sampling（LHS）. This method derives fracture data from the photographs of both exposed surface layer and boreholes using camera technologies, then applies LHS to stochastic simulation of fracture parameters including their location, size, quantity, and attitude, and finally constructs a 3-D fracture network model in the frame of Visual Geo. It was applied to a hydropower project in China, along with the Monte Carlo method for comparison. The results show its advantages of lower computational cost and more accurate predictions of the field test values for dam foundation stability analysis.
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