改进DE-TMCMC法及其在高级模型参数识别上的应用

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.