A Novel Real-Time Method for Structural Damage Detection Using Kalman Filter and Sensitivity Analysis

Abstract

Damage detection is a critical aspect of structural health monitoring. The Kalman filter (KF) has been used in various methods for detecting structural damage based on online measurement data. Due to noise in the sensor responses and errors in the damage detection process, the damage detection process’s accuracy is reduced. This study proposes a novel method for resolving this issue by integrating the Kalman filter and sensitivity analysis (KFSA). The damage detection problem has been considered as a state estimation problem in this method, with the system’s state estimated using the KF. In this problem, the damage is considered a random phenomenon in the state equation, and the damage detection equation is used as the observation equation via sensitivity analysis (SA). Due to the randomization of the state equation and the resulting increase in accuracy, damages are accurately detected among the suspected damaged elements obtained via modal strain energy. The numerical validation of structures is carried out under various damage scenarios. The comparative results demonstrate that the proposed method for detecting damage is exact and can be used for the structural health monitoring of in-service civil structures.