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When traditional response surface method is used to evaluate the reliability of concrete-filled steel tubular arch bridge, due to its complex structure and highly nonlinear implicit function, the response surface fitting accuracy is not high, and the reliability accuracy is difficult to meet the requirements of design specifications. In order to solve the above problems, this paper chooses dynamic Bayesian networks (DBN) which is suitable for solving the problem of multiple state unit or system uncertainty to build implicit function of the response surface function. And this paper combines DBN and particle swarm optimization algorithm based on simulated annealing algorithm (PSOSA) to improve efficiency of model parameter optimization. It can make the construction of implicit function simulate the real structure of the limit state function. Then this paper verifies the suitability for this kind of complex structure reliability assessment of DBN-PSOSA hybrid algorithm. A numerical example is given to demonstrate the effectiveness of the proposed method, and the reliability of a concrete filled steel tube arch bridge project is evaluated. The results show that this method improves the calculation accuracy and efficiency.

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The field load test is a direct and effective method for evaluating the performance of bridge structures. However, the existing bridge static load tests on site are costly, inefficient, and obstruct traffic; moreover, improper loading may also cause some damage to the bridge structure. This paper proposes a random model update method based on bridge dynamic load tests and the Bayesian inference as an alternative to the static load test. The Gaussian process model was used instead of the finite element model to reduce the cost of model calculation. Furthermore, choose the Markov Chain Monte Carlo (MCMC) method based on delay rejection adaptive Metropolis algorithm for Bayesian inference to improve the speed of the Bayesian method inferring the posterior probability density of updated parameters. First, the parameters to be updated for the bridge structure analysis model were determined based on the global sensitivity analysis method. Second, a uniform design sampling method was used to establish the Gaussian process optimization model to update the random model of the bridge structure. Finally, a reinforced concrete truss arch bridge was used to verify the correctness of the static load results of the bridge predicted by the random model update method based on dynamic load testing and Bayesian inference. The results show that the predicted results of the bridge static load test based on the dynamic load test and Bayesian reasoning method have an excellent agreement with the measured results, and this method can effectively overcome the adverse effects of the existing bridge static load test.

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