Bridge full-field response reconstruction method based on hybrid monitoring theory

Kaiman filter (KF) and maximum a posteriori (MAP) are two common generalized Bayesian filtering algorithms in structural load identification. KF method has high computational efficiency but poor numerical stability, while MAP method has strong applicability but requires eomplex matrix inversion Operations. In addition, these two methods have strict requirements for load form and measurement point arrangement, and are currently only suitable for identifying simple loads. Here, a Bayesian full-field response reconstruction method was proposed for arbitrary distributed loads, it could improve existing methods from both online and offline perspectives. For the online KF method, equivalent load vectors were derived from structural dynamic characteristics to reduce the dimension number of unknown loads, obtain an equivalent System model to satisfy controllability conditions, and use input State Joint estimation method for simultaneously identifying equivalent loads and global responses. For the offline MAP method, a load prior distribution considering spatial correlation was introduced, and MAP strategy was used to simultaneously iteratively estimate equivalent load and observed noise. Then, the global response was reconstructed according to the identified equivalent load. The improved online and offline methods could not require prior acquisition of load positions or distribution forms. Finally, the accuracy and applicability of the proposed method were verified with collected response data of Qingzhou Bridge under wind and traffic loads. © 2025 Chinese Vibration Engineering Society. All rights reserved.