Damage identification of a large cable-stayed bridge with novel cointegrated Kalman filter method under changing environments

Abstract

Damage identification is an indispensable part for successive applications of structural health monitoring. In practical applications, however, time-varying environmental and operational conditions, such as temperature and external loadings, often overwhelm the subtle structural changes caused by damage. It is therefore of great significance to remove those structural changes (damage features) caused by external influences from actual structural damage. In this paper, a new damage identification method based on Kalman filter and cointegration (KFC) is developed, and the environmental effects on damage indicator are removed by the cointegration process of the Kalman filtered coefficients. The cointegration relationship between structural frequencies is first established with augmented Dickey–Fuller test and Johansen procedure. The cointegration coefficients are then used to constitute the Kalman filter (KF) state vector, and the recursive KF process is intrigued to on-line estimate the change of structure states. To enhance the importance of incoming new observations in the KF, we introduce an adaptive fading factor into the conventional KF. Numerical simulation of a truss bridge is used to validate the effectiveness of the proposed KFC method for damage identification under varying temperature, even with 10% noise. Finally, the KFC method is applied to a cable-stayed bridge built in China (Tianjin Yonghe Bridge), and two structural damage scenarios are successfully identified. The advantages of the proposed KFC method are its ability to eliminate ambient temperature influences and identify structural damage on-line.