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通过建立不同赋存水体深度、不同隧道直径和不同开挖距离等共96个复杂裂隙网络数值模型,计算通过隧道掌子面的涌水量,拟合预测隧道涌水量的多参数回归方程。结果表明,流体的主要流动路径由连接入口边界和出口边界的连通裂隙组成。组成主要流动路径的裂隙内的流速最大;离连接入口边界和出口边界的连通裂隙越远,裂隙内的流速越小;水体较浅时,涌水量与开挖距离具有幂函数关系;水体较深时,涌水量与开挖距离具有线性关系;对隧道涌水量影响程度由高到低的影响因素依次为隧道直径、水体深度、开挖距离。隧道涌水量的预测结果和数值计算结果能很好吻合,验证了该回归方程具有普适性,可对节理发育岩体内隧道涌水量进行动态预测。
A total of 96 complex fracture network numerical models are established, including the depth of different water bodies, different tunnel diameters and different excavation distances, and the multi-parameter regression equations for predicting water inflow from the tunnel face are calculated. The results show that the main flow path of the fluid consists of communicating fractures connecting the inlet boundary and the outlet boundary. The fissures that make up the main flow path have the largest flow velocities. The farther the fissures are, the farther away from the connecting fissures connecting the boundary of the inlet and the boundary of the exit. The fissures have a power function when the water is shallow, , There was a linear relationship between water inflow and excavation distance. The influencing factors of descending tunnel water inflow from high to low were tunnel diameter, water depth and excavation distance. The prediction results of tunnel water inflow are in good agreement with the numerical results, which verify the universality of the regression equation and can predict the tunnel water inflow in the jointed rock mass dynamically.