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首页> 外文期刊>Advances in civil engineering >Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling-Induced Ground Settlements
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Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling-Induced Ground Settlements

机译:跳跃地质力学参数顺序概率后面分析的贝叶斯探讨性探讨性接地沉降

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This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. Within the framework of the proposed approach, a complex Bayesian back analysis problem is transformed into an equivalent structural reliability problem based on subset simulation. Hermite polynomial chaos expansion-based surrogate models are constructed to improve the computational efficiency of probabilistic back analysis. The reliability of tunneling-induced ground settlements is updated in the process of sequential back analyses. A real shield tunnel project of No. 1 Nanchang Metro Line in China is investigated to assess the effectiveness of the approach. The proposed approach is able to infer the posterior distributions of uncertain geomechanical parameters (i.e., Young’s moduli of surrounding soil layers and ground vehicle load). The reliability of tunneling-induced ground settlements can be updated in a real-time manner by fully utilizing the time-series monitoring data. The results show good agreement with the variation trend of field monitoring data of ground settlement and the post-event investigations.
机译:本文提出了一种新的顺序概率回分析方法,用于使用时间序列监测数据确定屏蔽隧道的不确定地质力学参数。该方法是基于最近开发的贝叶斯更新与子集模拟的更新。在所提出的方法的框架内,基于子集仿真,将复杂的贝叶斯回分析问题变为等效的结构可靠性问题。构建了基于Hermite多项式混沌扩展的替代模型,提高了概率后分析的计算效率。隧道引起的地面定居点的可靠性在顺序后分析过程中更新。调查了中国第1号南昌地铁线的真正盾构隧道项目,以评估该方法的有效性。所提出的方法能够推断不确定的地质力学参数的后部分布(即,杨氏的周围土壤层和地面车辆负荷的杨氏模数)。可以通过充分利用时序监测数据,以实时方式更新隧道引起的地沉降的可靠性。结果表明,与地面结算数据及事后调查的现场监测数据的变化趋势良好。

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