Real-time identification of time-varying tension in stay cables by monitoring cable transversal acceleration

Abstract

SUMMARYStay cables are critical components in bridges. However, stay cables suffer from severe fatigue damage. Therefore, a monitoring technique to obtain the time history of the tension in stay cables is important. Because the acceleration of stay cables is readily measurable, approaches to identify cable tension based on frequency analysis and monitored cable acceleration have been widely investigated and used in practice. However, this type of approach can only identify a time‐invariant tension of a stay cable over a specified duration, not the time‐varying tension. This paper proposes an approach to identify the time‐varying tension of stay cables by monitoring cable accelerations. The tension variation in stay cables is caused by vehicles passing over the bridge. The real‐time identification algorithm that determines the time‐varying tension of stay cables is proposed using an extended Kalman filter based on both the transversal monitored acceleration at a single location on the cable and the monitored wind speed on the bridge, where the time‐varying tension is a state variable that is identified. A stay cable from the Nanjing Yangtze River No. 3 Bridge was used for the numerical study. The time‐varying tension of the stay cable can be identified when either a single vehicle or multiple vehicles pass over the bridge. The robustness of the proposed approach is also investigated through deviations in the initial tension, initial displacement, and velocity of the stay cable. An experiment was conducted on a scaled stay cable with time‐varying tension excited by wind. The time‐varying cable tension of the cable was identified by the proposed approach and compared with the real time‐varying cable tension. The identification accuracy and robustness of the proposed approach is verified through the experiment and numerical study. Copyright © 2013 John Wiley & Sons, Ltd.

The state of the art in structural health monitoring of cable-stayed bridges

“…Therefore, the cable tension force identified by this technique is an average value for a period of time rather than the real-time cable tension force. Li et al [55] proposed a model-based identification algorithm for time varying cable tension force using an extended Kalman filter (EKF) based on the acceleration of a cable. The accuracy and robustness of this algorithm have been investigated through numerical study and scale cable tests.…”

Synthesize identification and control for smart structures with time‐varying parameters under unknown earthquake excitation

“…Due to severe hazards such as earthquakes and environmental erosion, structural physical parameters may inevitably change with time. Therefore, it is essential to research on the identification of time‐varying systems . However, as indicated in the state‐of‐the‐art review by Wang et al, identification of time‐varying structural system is less established than that of time‐invariant structures, although some time‐frequency analysis tools such as Hilbert transform and Hilbert–Huang transform, variational mode decomposition have been developed for identification of time‐varying structural systems, these tools can only provide empirical information.…”

“…Therefore, it is essential to research on the identification of time-varying systems. 1,2 However, as indicated in the state-of-the-art review by Wang et al, 3 identification of time-varying structural system is less established than that of time-invariant structures, although some time-frequency analysis tools such as Hilbert transform and Hilbert-Huang transform, [4][5][6][7][8] variational mode decomposition 9 have been developed for identification of time-varying structural systems, these tools can only provide empirical information. Also, due to its strong capability, wavelet transform has been utilized to identify the time-varying structural parameters.…”

Estimation of full‐field, full‐order experimental modal model of cable vibration from digital video measurements with physics‐guided unsupervised machine learning and computer vision

“…Therefore, a full‐field, full‐order or high spatio‐temporal resolution dynamics (modal) model of cable vibration is established by the proposed method using only the video of the cable. Furthermore, this new approach also provides a low‐cost, noncontact, remote sensing technique to automatically and efficiently estimate the cable tension using only the video of the vibrating cable, as compared with traditional contact‐type methods (e.g., load cells, optical fiber sensors, and accelerometers) that are fragile, expensive, and require extensive time and labor for installment, or other noncontact sequential measurement techniques and imaging methods (e.g., Digital Image Correlation (DIC)) that typically require installing markers or speckle paints on the cable . For validations, laboratory experiments on a bench‐scale cable excited by impact or wind load are conducted, and the results are compared with traditional measurement and analysis methods.…”