REMAINING LOAD CAPACITY ESTIMATION OF BRIDGE STRUCTURES BASED ON THE BAYESIAN UPDATED PHYSICAL PARAMETERS

Taking the randomness of system parameters into account, this paper presents a two-step remaining load capacity estimation for bridge structures based on the Bayesian updated physical parameters. In the first step, the sub-structure method and the Extended Kalman Filter algorithm are combined to identify the physical parameters of the substructure and the adjacent elements. In the second step, the Bayesian updating process is taken and the identified physical parameters are treated as the new conditional distribution information while the Monte-Carlo simulation parameters are treated as the prior distribution information. Then, the remaining load capacity estimation is procured based on the Monte-Carlo simulation parameters and the Bayesian updated physical parameters respectively. The numerical examples show that the remaining load capacity estimation based on the Bayesian updated physical parameters is much lower than that estimated using the Monte-Carlo simulation. The method provides a good solution for remaining load capacity estimation of bridges with incomplete response information and small samples.