岩土力学2025，Vol.46Issue(5)：1392-1408,17.DOI:10.16285/j.rsm.2024.1006
基于多元概率分布的土体硬化模型参数预测
Parameter predictions of hardening soil model based on multivariate probability distribution
摘要
Abstract
To address the challenge of determining parameters for the hardening soil(HS)model in engineering practice,a database named HS-CLAY/9/196,which includes HS parameters,is established.A multivariate probability distribution for HS parameters is constructed based on the database.The probability distributions of HS parameters are updated using the available measured data of common soil parameters.The effects of the types and quantities of measured soil parameters on the estimation of HS parameters are studied.In addition,the probabilistic transformation models of HS model parameters(i.e.,oedometric tangent stiffness Erefoed,triaxial secant stiffness Eref50,and unloading/reloading stiffness Erefur)are proposed based on the given measured data.The results show that the established multivariate probability distribution model effectively characterizes the statistical characteristics and cross-correlations of HS parameters.Based on the constructed multivariate probability distribution model,various measured data can be integrated to enhance the accuracy of parameter predictions through Bayesian updating.The prediction uncertainty can be reduced as the variety of measured data increases.To achieve accurate predictions for Erefoed,Eref50,and Erefur with low uncertainty,priority should be given to collecting the measured data of soil parameters that are strongly cross-correlated with the target HS parameters,such as the compressibility modulus Es1-2,water content w,and void ratio e.关键词
土体硬化模型/土性参数/数据库/多元概率分布/贝叶斯更新Key words
hardening soil model/soil parameters/database/multivariate probability distribution/Bayesian updating分类
建筑与水利引用本文复制引用
陶袁钦,潘孙珏徐,孙宏磊,聂艳侠..基于多元概率分布的土体硬化模型参数预测[J].岩土力学,2025,46(5):1392-1408,17.基金项目
国家重点研发计划(No.2023YFC3009400) （No.2023YFC3009400）
国家自然科学基金青年基金(No.42307218) （No.42307218）
浙江省"尖兵""领雁"研发攻关计划项目(No.2023C03176).This work was supported by the National Key R&D Program of China(2023YFC3009400),the National Natural Science Foundation of China(42307218)and the"Pioneer"and"Leading Goose"Key R&D Program of Zhejiang(2023C03176). （No.2023C03176）
