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Volume 41 Issue 12
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CHENG Ma-yao, JIN Yin-fu, YIN Zhen-yu, WU Ze-xiang. Enhanced DE-TMCMC and its application in identifying parameters of advanced soil model[J]. Chinese Journal of Geotechnical Engineering, 2019, 41(12): 2281-2289. DOI: 10.11779/CJGE201912013
Citation: CHENG Ma-yao, JIN Yin-fu, YIN Zhen-yu, WU Ze-xiang. Enhanced DE-TMCMC and its application in identifying parameters of advanced soil model[J]. Chinese Journal of Geotechnical Engineering, 2019, 41(12): 2281-2289. DOI: 10.11779/CJGE201912013
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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

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  • Received Date: July 25, 2018
  • Published Date: December 24, 2019
    Graphical Abstract
    • 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.
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    • References (22)
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