Structural damage identification based on Bayesian updating

Due to factors such as model accuracy, sensor arrangement, noise influence, eigenvalue selection and the influence of local damage on the overall response, the structural damage identification process inevitably has uncertainties, so it is necessary to introduce probabilistic statistical methods to improve its accuracy and robustness. In this paper, structural damage identification is carried out based on Bayesian updating. Bayesian updating is combined with real-time monitoring data (e.g. displacement, acceleration), and the real-time data reflect the current state of the structure and contain damage information. The experimental results show that the update results of the Bayesian method correlate well with the actual data, which verifies the feasibility of Bayesian updating in structural damage identification. The structural prior probability can be effectively updated by this method, which shows high accuracy and robustness in structural damage identification and is able to maintain stable performance under different working conditions and environments. Based on Bayesian updating, this structural damage identification method provides a new technical means for structural health monitoring and damage assessment, with broad application prospect and development space.