A Kalman Filter-Based Method for Diagnosing the Structural Condition of Medium- and Small-Span Beam Bridges during Brief Traffic Interruptions

Abstract

Load tests are a popular way to diagnose the structural condition of bridges, however, such tests usually interrupt traffic for many hours. To address this issue, a Kalman filter-based method is proposed to diagnose the structural condition of medium- and small-span beam bridges by using the acceleration responses obtained from the bridge during a brief traffic interruption. First, a condition diagnosis feature based on the Kalman filter innovation (i.e., the optimal difference between the filter predictions and measured responses) is presented. Second, a condition diagnosis index, which is the energy ratio between the innovation and the measured acceleration, is generated by calculating the null space of the Hankel matrix consisting of condition diagnosis features. Then, on the basis of the novel detection, a method is used to diagnose the structural condition of a bridge during a brief traffic interruption. Finally, the validity and dependability of the proposed method is demonstrated through experimental tests with a model bridge and field tests on an actual bridge. Using the proposed method, the long-time interruption of traffic flow and the reliance on finite element model are effective avoided during the process of condition diagnosis of bridges.

Strain Virtual Sensing Applied to Industrial Presses for Fatigue Monitoring

“…The Kalman Filter (KF) is a recursive Bayesian algorithm that estimates the hidden states of a system using a state-space model of the system and a limited number of measurements. This algorithm was first proposed by R. Kalman in 1960 [ 8 ], and since then, it has been used in a wide variety of fields, including the application of VS in machines and industrial structures [ 33 , 34 ].…”