A comparative study of Bayesian inverse analyses of spatially varying soil parameters for slope reliability updating

被引:20
作者
Jiang, Shui-Hua [1 ,2 ]
Wang, Lei [1 ]
Ouyang, Su [1 ]
Huang, Jinsong [1 ,3 ]
Liu, Yuan [1 ]
机构
[1] Nanchang Univ, Sch Civil Engn & Architecture, 999 Xuefu Rd, Nanchang 330031, Jiangxi, Peoples R China
[2] Wuhan Univ, Inst Engn Risk & Disaster Prevent, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Peoples R China
[3] Univ Newcastle, Coll Engn Sci & Environm, Discipline Civil Surveying & Environm Engn, Callaghan, NSW, Australia
基金
中国国家自然科学基金;
关键词
Spatial variability; Bayesian updating; DREAM method; BUS method; reliability analysis; PROBABILISTIC BACK-ANALYSIS; MONTE-CARLO-SIMULATION; DIFFERENTIAL EVOLUTION; FAILURE; VARIABILITY; ALGORITHM; FIELD;
D O I
10.1080/17499518.2021.2010098
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
Bayesian estimation of spatially varying soil parameters is a challenging task in geotechnical engineering because a large number of random variables need to be learned. To address this challenge, three Bayesian methods are revisited, including Differential Evolution Adaptive Metropolis with sampling from past states [DREAM((zs))] method, Bayesian Updating with Structural reliability methods using Subset Simulation (BUS + SS), and modified BUS with Subset Simulation (mBUS + SS). The differences between the performances (i.e. convergences, computational accuracies, and efficiencies) of these three methods are not well understood. This study systematically investigates the differences of these three methods in the generation of random samples, convergence criterion, model evidence, and estimation of posterior probability of failure in slope reliability updating. Two slope examples are used for the comparative study. It is found that the BUS + SS method performs well not only in the low-dimensional Bayesian inverse problems but also in the high-dimensional Bayesian inverse problems of spatially varying soil parameters. The DREAM((zs)) method is preferentially recommended to deal with the low-dimensional Bayesian inverse problems whereas the mBUS + SS method can well tackle the high-dimensional problems.
引用
收藏
页码:746 / 765
页数:20
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