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首页> 外文期刊>Tunnelling and underground space technology >A no-tension elastic-plastic model and optimized back-analysis technique for modeling nonlinear mechanical behavior of rock mass in tunneling
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A no-tension elastic-plastic model and optimized back-analysis technique for modeling nonlinear mechanical behavior of rock mass in tunneling

机译:隧道岩体非线性力学行为建模的无张力弹塑性模型和优化反分析技术

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摘要

In this paper, we present a no-tension elastic-plastic model and an optimized back-analysis technique for stability analysis of underground tunnels. A set of constitutive equations is presented to simulate the no-tension behavior and plastic yielding of jointed rock masses which yield according to the Drucker-Prager yield criterion and permits no-tension. A nonlinear 2-D finite element model is consequently formulated for the prediction of the behavior of the excavated rock mass. As for the model parameters, the genetic algorithm technique is employed to find the optimal rock mass properties by minimizing the discrepancy between the predicted results and field measurement. The nonlinear finite element model coupling with the genetic algorithm optimized back-analysis technique is then applied to a synthetic example of a deep tunnel in yielding rock. The results show that the forward and back-analysis system is capable of estimating the model parameters with stable and good convergence and give reasonable predictions. Numerical experiments are also carried out to check the influences of position and numbers of measurements to the reliability of the back-analysis results. Furthermore, the sensitivity analysis of the genetic algorithms optimization procedure is discussed in terms of identification of geo-material properties.
机译:在本文中,我们提出了地下隧道稳定性分析的无张力弹塑性模型和优化的反分析技术。提出了一组本构方程来模拟节理岩体的无拉力行为和塑性屈服,这些节理岩体根据Drucker-Prager屈服准则屈服并允许无拉力。因此,建立了非线性二维有限元模型来预测开挖岩体的行为。对于模型参数,通过最小化预测结果与现场测量之间的差异,采用遗传算法技术找到最佳岩体特性。然后,将非线性有限元模型与遗传算法优化的反分析技术相结合,应用于深部隧道在屈服岩层中的合成实例。结果表明,前向和后向分析系统能够稳定稳定地估计模型参数,并给出合理的预测。还进行了数值实验,以检查位置和测量次数对反分析结果的可靠性的影响。此外,从识别岩土材料的特性出发,讨论了遗传算法优化程序的敏感性分析。

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