深埋硬岩隧道围岩参数概率反演方法

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

在贝叶斯理论框架下,提出了一种基于多源数据融合的深埋硬岩隧道围岩参数概率反演方法.首先,分析硬岩隧道常用的启裂—剥落界限本构模型中围岩单轴抗压强度、启裂强度与抗压强度比及抗拉强度三个参数不确定性来源,确定其概率统计特征;其次,利用粒子群算法优化多输出支持向量机,建立反映反演参数与隧道监测数据间非线性映射关系的智能响应面;最后,结合贝叶斯分析方法构建概率反演模型,运用马尔科夫链蒙特卡洛模拟算法实现了围岩参数的动态更新.将该方法应用到某深埋硬岩隧道中,利用反演的围岩参数计算隧道拱顶下沉点、周边收敛点变化值及开挖损伤区深度,与监测数据吻合较好.结果表明,该方法可以实现围岩多参数快速概率反演,更新后的参数可用于硬岩隧道施工安全风险评估与结构可靠性设计.

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 MarkovChain 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.