改进DE-TMCMC法及其在高级模型参数识别上的应用

程马遥; 金银富; 尹振宇; 吴则祥

doi:10.11779/CJGE201912013

改进DE-TMCMC法及其在高级模型参数识别上的应用 English Version

1. 香港理工大学土木与环境工程系,香港;
2. 佛山科学技术学院土木工程系,广东佛山 528000;
3. 温州大学建筑工程学院,浙江温州 325035

基金项目: 国家自然科学基金面上基金项目（51579179）

详细信息

作者简介:
程马遥(1988— ),女,讲师,博士,主要从事土体细观力学与本构等方面的教学和科研。E-mail: chengmayao@163.com。

通讯作者:
尹振宇，E-mail：zhenyu.yin@polyu.edu.hk

中图分类号: TU43
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出版历程
- 收稿日期: 2018-07-25
- 发布日期: 2019-12-24

Enhanced DE-TMCMC and its application in identifying parameters of advanced soil model

1. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China;
2. Department of Civil Engineering, Foshan University, Foshan 528000, China;
3. College of Architecture and Civil Engineering, Wenzhou University, Wenzhou 325035, China

摘要

摘要: 目前基于贝叶斯结合马尔可夫链蒙特卡罗（MCMC）的参数识别方法仅在某些传统的简单本构模型的参数识别上得到了验证。鉴于此,提出了一种效率更高的基于差分进化算法的过渡马尔可夫链蒙特卡罗方法（DE-TMCMC）,并基于此提出了一种高效的贝叶斯参数识别方法,应用于高级土体本构模型的参数识别。为了验证其稳健性和有效性,选取丰浦砂的常规室内试验结果作为目标试验来识别考虑临界状态的砂土本构模型的参数。通过对比原始TMCMC方法在参数识别上的表现,突显了DE-TMCMC在识别砂土高级本构模型参数方面的能力。
- 砂土 /
- 本构关系 /
- 参数识别 /
- 不确定性 /
- 贝叶斯理论
Abstract: The parameter identification using Bayesian approach with Markov chain Monte Carlo (MCMC) has been verified only for certain conventional simple constitutive models up to now. An enhanced version of the differential evolution transitional Markov chain Monte Carlo (DE-TMCMC) method and a competitive Bayesian parameter identification approach for use in advanced soil models are presented. The DE-TMCMC, enhanced through implementing a differential evolution into TMCMC to replace the process of proposing a new sample, is proposed. To verify its robustness and effectiveness, the triaxial tests on Toyoura sand are selected as objectives to identify the parameters of the critical state-based sand model SIMSAND. The original TMCMC is also used as a reference to compare the results of DE-TMCMC, which indicates that the DE-TMCMC is highly robust and efficient in identifying the parameters of advanced soil models. All the results demonstrate the excellent ability of the enhanced Bayesian parameter identification approach in identifying the parameters of advanced soil models from both laboratory and in situ tests.
- sand /
- constitutive relation /
- parameter identification /
- uncertainty /
- Bayesian theory

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参考文献(22)

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