Experiment Research on Multi-model Structural Identification Based on Bayesian Theory

ZHOU Yun, JIA Fanding, XI Shuhang


The issue related to multi-model structural identification (MM St-Id) was experimentally researched based on sampling method of Bayesian theory. The concept and basic framework of MM St-Id method based on Bayesian theory were introduced, and then, the Markov chain - Monte Carlo simulation (MCMC) was utilized to build finite element (FE) model libraries. Since MCMC is not easy to converge and it has low calculation efficiency when the parameters have high dimensions, an improved MCMC sampling method for MM St-Id was introduced. The Matlab-Strand7 Application Programming Interface (API) strategy can be used to update the parameters of large structural FE model automatically. After the calibrated FE model libraries were established, they can be used to predict the responses based on the posterior probability distribution of the FE models. In order to verify the feasibility and effectiveness of the proposed theory, a numerical example of a simply-supported beam and an on-site large concrete-steel tubular truss arch bridge St-Id were investigated based on Bayesian theory and response prediction. A simple model St-Id method -genetic algorithm (GA) was used for comparison. The results showed that the proposed MM St-Id method based on Bayesian theory was much better in structural response prediction.



Keywords: structural identification (St-Id),  multi-model, method,  Bayesian theory,  MCMC,  bridge structures

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