基于监测数据的边坡参数统计特征及可靠度更新

Parameters Statistics and Reliability Updating of Slopes Based on Monitoring Data

掌握详细的边坡岩土体参数信息是进行边坡加固与风险评估的重要前提。目前大多采用马尔可夫链蒙特卡洛模拟方法基于现场或室内试验数据进行不确定性参数统计信息及边坡可靠度更新。然而,该方法存在计算量大、计算结果不易收敛和难以解决高维边坡不确定性参数统计特征及可靠度更新等问题。本文基于粒子群优化反向传播神经网络算法建立边坡位移代理模型优化计算过程,提出了改进的基于子集模拟的贝叶斯更新方法,进而基于边坡变形监测数据进行参数统计特征及可靠度更新。最后,将提出方法应用到长春西客站深基坑边坡工程。结果表明:提出方法能够融合有限的场地信息有效地更新边坡岩土体参数统计特征,推断其后验概率密度函数,进而更新边坡失效概率。利用更新的土体参数计算的边坡位移与实测数据吻合,验证了提出方法的适用性和有效性。另外融合监测数据进行贝叶斯更新之后,虽然土体参数的不确定性明显降低,但是边坡失效概率受外界气温、监测点位置及数据量值的影响会增大。

Obtaining detailed information on soil parameters is a significant precondition for slope rein‐forcement and risk assessment.At present,the Markov chain Monte Carlo(MCMC)simulation is frequently used to update the statistical information of uncertain parameters based on in-situ and/or lab‐oratory test data,but it is difficult to solve the problem of high-dimensional slopes due to a large amount of calculation consumption and poor convergence.In this paper,a slope displacement surro‐gate model based on particle swarm optimization back propagation neural network is constructed to ac‐celerate the calculation process.An improved Bayesian updating with subset simulation(BUS)is pro‐posed for updating the statistics of soil parameters and slope reliability based on the monitoring data of slope displacement.The proposed method is then applied to a practical slope project(Changchun West Railway Station's Deep Foundation Pit Slope Project).The results indicate that the proposed method can effectively update the statistics of soil parameters,infer their posterior probability distribu‐tion,and further update the probability of slope failure.Then,the slope displacement evaluated using the updated soil parameters agree well with the measured data,which confirms the applicability and ef‐fectiveness of the proposed method.Additionally,after the Bayesian updating with the monitoring da‐ta,the uncertainties of soil shear strength parameters are significantly reduced,but the probability of slope failure can be increased due to the influences of the ambient temperature,monitoring positions and values of monitoring data.