毋振华, 王者超, 郭玟志, 等.2020.平面交叉裂隙非线性渗流模型参数人工神经网络预测[J].工程地质学报, 28(5): 982-988. doi: 10.13544/j.enki.jeg.2020-184.
    引用本文: 毋振华, 王者超, 郭玟志, 等.2020.平面交叉裂隙非线性渗流模型参数人工神经网络预测[J].工程地质学报, 28(5): 982-988. doi: 10.13544/j.enki.jeg.2020-184.
    Wu Zhenhua, Wang Zhechao, Guo Wenzhi, et al. 2020. Artifcial neural network prediction of parameters of non-linear seepage model with planar intersecting fractures[J]. Jourmal of Engineering Geology, 28(5): 982-988. doi: 10.13544/j.cnki.jeg.2020-184.
    Citation: Wu Zhenhua, Wang Zhechao, Guo Wenzhi, et al. 2020. Artifcial neural network prediction of parameters of non-linear seepage model with planar intersecting fractures[J]. Jourmal of Engineering Geology, 28(5): 982-988. doi: 10.13544/j.cnki.jeg.2020-184.

    平面交叉裂隙非线性渗流模型参数人工神经网络预测

    ARTIFICIAL NEURAL NETWORK PREDICTION OF PARAMETERS OF NON-LINEAR SEEPAGE MODEL WITH PLANAR INTERSECTING FRACTURES

    • 摘要: 岩体裂隙网络渗流广泛存在于地下工程中,对地下工程的建设和运行安全具有重要的影响。因此,研究裂隙网络渗流有着重要的理论和实际意义。本文根据立方定律和Forchheimer方程推导所得的交叉裂隙渗流模型,运用数值模拟和人工神经网络方法,对平面交叉裂隙渗流模型非线性参数与模型几何条件的关系进行探究。通过数值模拟,获得了平面交叉裂隙非线性渗流模型的参数;运用人工神经网络遗传算法,探究了交叉裂隙几何条件与交叉裂隙渗流模型中非线性系数之间的关系,证明了平面交叉裂隙非线性渗流模型适用于描述交叉裂隙渗流规律,验证了神经网络方法预测非线性系数的可行性和准确性。同时,还对比分析了运用拟合数值表达式和人工神经网络两种方法的特点。

       

      Abstract: The fluid flow in rock fracture network exists widely in underground engineering,which has important influences on the safety of engineering construction and operation. Therefore,the study of flow in fracture network has important theoretical and practical significance. This paper is based on the flow model of intersecting fractures. The model is derived based on the cubic law and Forchheimer equation. It studies the nonlinear parameters of the flow model of planar intersecting fractures by numerical simulation and artificial neural network. Using the artificial neural network genetic algorithm,the paper explores the relationship between the geometry of the intersecting fractures and the nonlinear coefficient in the flow model. It proves that the nonlinear flow model of the plane intersecting fractures can adequately describe the flow characteristics of the intersecting fractures. It verifies the feasibility and accuracy of the neural network method in predicting the nonlinear coefficient. At the same time,it obtains the characteristics of the two methods of fitting numerical expression and artificial neural network.

       

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