Inference of probability distributions of geotechnical parameters using 
adaptive Bayesian updating approach

JIANG Shui-hua; FENG Ze-wen; LIU Xian; JIANG Qing-hui; HUANG Jin-song; ZHOU Chuang-bing

doi:10.16285/j.rsm.2018.2266

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Inference of probability distributions of geotechnical parameters using adaptive Bayesian updating approach

更新时间：2026-06-25

- Inference of probability distributions of geotechnical parameters using adaptive Bayesian updating approach
- Vol. 41, Issue 1, Pages: 325-335(2020)
- 作者机构：
  
  南昌大学建筑工程学院,江西,南昌,330031
- 作者简介：
- 基金信息：
- DOI：10.16285/j.rsm.2018.2266
  CLC： TU41
- Online First：05 January 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
JIANG Shui-hua, FENG Ze-wen, LIU Xian, et al. Inference of probability distributions of geotechnical parameters using adaptive Bayesian updating approach[J]. 2020, 41(1): 325-335.
DOI：

JIANG Shui-hua, FENG Ze-wen, LIU Xian, et al. Inference of probability distributions of geotechnical parameters using adaptive Bayesian updating approach[J]. 2020, 41(1): 325-335. DOI： 10.16285/j.rsm.2018.2266.

摘要

合理推断岩土参数概率模型是岩土工程可靠度分析与风险评估的重要一步，目前大多基于现场或室内试验数据推断岩土参数概率分布。提出了岩土力学参数概率分布推断的自适应贝叶斯更新方法，建立了定量的子集模拟计算终止条件，构建了岩土参数概率分布推断及可靠度分析框架，并给出了计算流程。以台湾3号高速公路滑坡及不排水饱和黏土边坡为例验证了提出方法的有效性，并探讨了子集模拟每层样本数目对岩土参数概率分布推断的影响。结果表明：与最大似然和马尔可夫链蒙特卡洛方法相比，提出的方法计算效率高，编程较为简便，可为解决低接受概率水平岩土参数概率分布推断问题提供一个有效的工具。子集模拟每层随机样本数目对概率分布推断具有一定的影响，随着样本数目的增加，岩土参数后验统计特征和子集模拟阈值均逐渐收敛。此外，可根据互补累积分布函数随子集模拟阈值的变化关系来验证所建立的定量的子集模拟计算终止条件的合理性。

Abstract

Accurate inference of the probability distributions of geotechnical parameters is a crucial step for reliability analysis and risk assessment in geotechnical engineering. At present

the probability distributions of geotechnical parameters are mainly inferred based on in-situ and/or laboratory test data. This paper aims to propose an adaptive Bayesian updating approach for the probability distribution inference of geotechnical parameters

in which a quantitative termination strategy for subset simulation is presented. Moreover

a framework for the inference and reliability analysis of the probability distributions of geotechnical parameters is constructed. The effectiveness of the proposed approach is verified by taking the landslide on No. 3 Freeway in Taiwan and a saturated clay slope as examples. Finally

the influence of the number of samples in each subset simulation level on the inference of probability distributions is addressed in this paper. The results indicate that

in comparison with the maximum likelihood and Markov chain Monte Carlo methods

the proposed approach is more efficient in calculation

simpler in programming

and can provide an effective way to solve the problem of probability distribution inference of geotechnical parameters at low acceptance probability levels. The number of random samples in each subset simulation level has certain influence on probability distribution inference. As the number of samples in each level increases

the posterior statistics of geotechnical parameters and threshold of subset simulation gradually converge. In addition

the rationality of the established quantitative termination strategy for subset simulation can be verified according to the variation of complementary cumulative distribution function with the subset simulation threshold.

关键词

Keywords

references

Views

521

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Reliability updating for infinite soil slopes using BUS method

Local average method of triangular elements for discretization of random field of geotechnical parameters

Parameter predictions of hardening soil model based on multivariate probability distribution

Assessment of probability distribution of large samples of geotechnical parameters by using normal information spread estimation method

Slope risk assessment using efficient random finite element method

Related Author

JIANG Shui-hua

YAO Chi

YANG Jian-hua

ZHOU Chuang-bing

XIA Li-jiang

YIN Qi-xiang

ZHOU Guo-qing

WANG Tao

Related Institution

State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

School of Mechanics and Civil Engineering, China University of Mining and Technology

State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology

China Construction First Group Construction & Development Co., Ltd.

Key Laboratory of Civil Engineering Structure & Disaster Prevention and Mitigation Technology of Zhejiang Province

AI问答

⁰