An in-time damage identification approach based on the Kalman filter and energy equilibrium theory

Huang, Xing‐Huai; Dyke, Shirley J.; Xu, Zhao‐Dong

doi:10.1631/jzus.a1400163

Cited by 3 publications

(1 citation statement)

References 33 publications

(29 reference statements)

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“…Additionally, some methods have been combined with the Kalman filter to enhance the ability to detect structural damage. Based on energy theory, a relationship between the structural stiffness and acceleration response was generated by using Kalman filter, and this relationship was applied to fast detect the damage of structures [34]. An extended Kalman filter-based artificial neural network was proposed to detect the damage in bridges caused by the changes of environmental temperature [35].…”

Section: Introductionmentioning

confidence: 99%

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

Gao

Xiang²,

Liu

2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%