基于贝叶斯更新的非饱和土坡参数概率反演及变形预测  

Probabilistic back analysis of soil parameters and displacement prediction of unsaturated slopes using Bayesian updating

作　　者：仉文岗 顾鑫[2] 刘汉龙 张青[4] 王林[1,2,3] 王鲁琦 ZHANG Wen-gang;GU Xin;LIU Han-long;ZHANG Qing;WANG Lin;WANG Lu-qi(Key Laboratory of New Technology for Construction of Cities in Mountain Area,Chongqing University,Chongqing 400045,China;School of Civil Engineering,Chongqing University,Chongqing 400045,China;National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas,Chongqing University,Chongqing 400045,China;Center for Hydrogeology and Environmental Geology,China Geological Survey,Baoding,Hebei 071051,China)

机构地区：[1]重庆大学山地城镇建设与新技术教育部重点实验室,重庆400045 [2]重庆大学土木工程学院,重庆400045 [3]重庆大学库区环境地质灾害防治国家地方联合工程研究中心,重庆400045 [4]中国地质调查局水文地质环境调查中心,河北保定071051

出　　处：《岩土力学》2022年第4期1112-1122,共11页Rock and Soil Mechanics

基　　金：国家重点研发计划重点专项(No.2019YFC1509605);国家自然科学基金(No.52108299,No.52008058)。

摘　　要：滑坡变形预测一直是实现滑坡灾害预报与防控的有效手段。岩土体参数是开展滑坡变形计算的关键输入信息,然而目前研究鲜有考虑岩土体参数不确定性对滑坡变形的影响,如何融合有限监测数据实现岩土体参数不确定性定量表征及滑坡变形概率预测仍然是一大难点。以降雨入渗非饱和土坡为例,开展流固耦合分析,基于有限的孔压监测数据,利用DREAM_zs算法实现对岩土体参数的高效概率反演。根据岩土体参数的先验分布,采用拉丁超立方抽样法生成随机样本,将其导入ABAQUS中计算相应的坡脚变形作为数据集,分别采用多元自适应回归样条曲线(MARS)和Light GBM模型构建基于数理-机制双驱动的边坡坡脚变形预测模型,计算贝叶斯更新后的后验稳态样本对应的边坡坡脚变形值,并对边坡变形值开展统计分析。结果表明:DREAM_zs算法仅需少量的孔压监测数据,即可完成对岩土体参数的更新,并且计算效率高、收敛速度快。此外,提出的边坡坡脚变形预测模型不仅突破了由孔压等间接监测数据来预测边坡变形的局限,同时还实现了对边坡变形发生概率的预测,为滑坡变形预测提供了新的思路和探索。Displacement prediction has always been an effective means for conducting the prediction and prevention of landslide disasters.Geotechnical parameters are key input information for landslide deformation calculation,and are rarely considered in the current study.How to conduct the probabilistic prediction of the landslide deformation based on the quantitative characterization of the uncertainties in geotechnical parameters with limited monitoring data is still a prominent difficulty.A case of unsaturated soil slope under rainfall infiltration is investigated and the coupled hydro-mechanical analysis is performed.Based on the spare monitored pore water pressure data,the probabilistic back analysis of geotechnical parameters is efficiently accomplished via the DREAM_zs algorithm.A set of random samples are obtained via the Latin hypercube sampling in accordance with the prior distribution of the geotechnical parameters,and they are utilized to calculate the displacements at the slope toe through the numerical software ABAQUS.Then,a coupled numerical-mechanical displacement prediction model is established through the multiple adaptive regression splines(MARS)and LightGBM algorithm.Based on this model,the displacements at the slope toe are predicted with the stationary posterior samples and the statistical analysis is performed accordingly.It is found that the DREAM_zs algorithm can perform the probabilistic back analysis of geotechnical parameters using limited monitored data with high efficiency and fast convergence.In addition,the proposed displacement prediction model breaks through the limitation of displacement prediction with indirect monitored data such as pore water pressure.And the occurrence probability of slope deformation is also obtained.Furthermore,this study provides a novel idea and attempt for slope deformation prediction.

关 键 词：贝叶斯更新 非饱和土 流固耦合分析 机器学习 变形预测模型 

分 类 号：TU42[建筑科学—岩土工程]