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深埋硬巖隧道圍巖參數概率反演方法

Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel

  • 摘要: 在貝葉斯理論框架下, 提出了一種基于多源數據融合的深埋硬巖隧道圍巖參數概率反演方法.首先, 分析硬巖隧道常用的啟裂-剝落界限本構模型中圍巖單軸抗壓強度、啟裂強度與抗壓強度比及抗拉強度三個參數不確定性來源, 確定其概率統計特征; 其次, 利用粒子群算法優化多輸出支持向量機, 建立反映反演參數與隧道監測數據間非線性映射關系的智能響應面; 最后, 結合貝葉斯分析方法構建概率反演模型, 運用馬爾科夫鏈蒙特卡洛模擬算法實現了圍巖參數的動態更新.將該方法應用到某深埋硬巖隧道中, 利用反演的圍巖參數計算隧道拱頂下沉點、周邊收斂點變化值及開挖損傷區深度, 與監測數據吻合較好.結果表明, 該方法可以實現圍巖多參數快速概率反演, 更新后的參數可用于硬巖隧道施工安全風險評估與結構可靠性設計.

     

    Abstract: A large number of tunnel projects are being constructed or will be constructed in the mountainous areas of western China. However, they are several safety challenges in the construction of deep hard rock tunnels because of the complex topographic and geological conditions, strong geological tectonic activities, large burial depth, and high in situ stress level. Uncertainty of tunnel wall parameters is one of main factors that contribute to tunnel construction risk. The traditional deterministic back analysis method cannot reflect the uncertainty characteristics of tunnel wall parameters; therefore, within the framework of Bayesian theory, a probabilistic back analysis method based on integrating multi-source monitoring information was proposed for determining the surrounding rock parameters of deep hard rock tunnel. First, the uncertainty sources of three parameters——uniaxial compressive strength (UCS), crack initiation stress to UCS ratio, and tensile strength for the widely used damage initiation and spalling limit approach——were analyzed, and their probabilistic statistical characteristics were determined. Second, a multi-output support vector machine (MSVM) was optimized by particle swarm optimization (PSO) algorithm, and an intelligent response surface model was established to reflect the nonlinear mapping relationship between back-analyzed parameters and field monitoring data. Last, by combination with the Bayesian (B) analysis method, the B-PSO-MSVM model was established, and surrounding rock parameters were dynamically updated by applying the Markov Chain Monte Carlo simulation algorithm. The method was applied to a deep hard rock tunnel, and parameters from probabilistic back analysis were utilized to calculate the point change of the tunnel vault settlement and peripheral displacement convergence as well as the depth of excavation damage zones, and the results agreed well with the actual monitoring data. It is shown that this method can be used to back analyze multi parameters of surrounding rock quickly and probabilistically, and parameters updated can be applied for risk assessment in construction safety and structural reliability design for the hard rock tunnel.

     

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