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Acta Geotechnica

, Volume 14, Issue 6, pp 1925–1947 | Cite as

Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method

  • Yin-Fu Jin
  • Zhen-Yu YinEmail author
  • Wan-Huan Zhou
  • Suksun Horpibulsuk
Research Paper
  • 123 Downloads

Abstract

Parameter identification using Bayesian approach with Markov Chain Monte Carlo (MCMC) has been verified only for certain conventional simple constitutive models up to now. This paper presents an enhanced version of the differential evolution transitional MCMC (DE-TMCMC) method and a competitive Bayesian parameter identification approach for applying to advanced soil models. To realize the intended computational savings, a parallel computing implementation of DE-TMCMC is achieved using the single program/multiple data technique in MATLAB. To verify its robustness and effectiveness, synthetic numerical tests with/without noise and real laboratory tests are used for identifying the parameters of a critical state-based sand model based on multiple independent calculations. The original TMCMC is also used for comparison to highlight that DE-TMCMC is highly robust and effective in identifying the parameters of advanced sand models. Finally, the proposed parameter identification using DE-TMCMC is applied to identify parameters of an elasto-viscoplastic model from two in situ pressuremeter tests. All results demonstrate the excellent ability of the enhanced Bayesian parameter identification approach on identifying parameters of advanced soil models from both laboratory and in situ tests.

Keywords

Bayesian parameter identification Constitutive model Clay Pressuremeter Sand Transitional Markov chain Monte Carlo 

Notes

Acknowledgements

This research was financially supported by a RIF project (Grant No.: PolyU R5037-18F) from Research Grants Council (RGC) of Hong Kong Special Administrative Region Government (HKSARG) of China, and the National Natural Science Foundation of China (Grant No. 51579179).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yin-Fu Jin
    • 1
  • Zhen-Yu Yin
    • 1
    Email author
  • Wan-Huan Zhou
    • 2
  • Suksun Horpibulsuk
    • 3
  1. 1.Department of Civil and Environmental EngineeringThe Hong Kong Polytechnic UniversityHung Hom, KowloonChina
  2. 2.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental EngineeringUniversity of MacauMacauChina
  3. 3.School of Civil EngineeringSuranaree University of TechnologyMuang DistrictThailand

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