Bridge cables vibration frequency identification and fatigue life prediction based on SHM data with machine learning
Kai Cao,Yang Ding,Hui Li,Tong-Lin Yang,Gang-Gui Liu,Tian-Yun Chu
Abstract
Cables are an essential component of bridge structures, and their safety is directly related to the service life of the bridge. In particular, the change in cable force is a crucial indicator for assessing the safety of the cable. In this paper, the acceleration signal of the cable structure is obtained using a structural health monitoring (SHM) system, and the signal is transformed into the time-domain frequency using the Fourier transform method (FFT). Then, the frequency-cable force equation is established to quickly and accurately obtain the change in cable force in the time domain. Finally, the statistics of the cable force range are obtained using the rainflow counting method. This method enables the rapid and accurate assessment of the fatigue life of the cable structure when physical parameters of the cable are available. In particular, a probability prediction model is constructed based on the Bayesian emulator, and the influence of three covariance functions (Squared Exponential (SE), Matern-3/2 (MA-3/2), and Matern-5/2 (MA-5/2)) on the prediction performance is discussed, which is verified using the SHM data. The results indicate that for acceleration signal data with significant environmental noise, the Bayesian model based on MA-3/2 covariance performs the best in terms of prediction. However, when the acceleration signal data is relatively smooth, the prediction performance of the Bayesian model based on the three covariance functions is the same. At the same time, by combining on-site monitoring data, it can be inferred that the fatigue life of the bridge stay cables is greater than the service life, meeting the safety requirements for bridge operation and maintenance.
Key Words
cable force assessment; covariance functions; fatigue life; fourier transform method; probability prediction model; rain flow counting; structural health monitoring
Address
Kai Cao — 1)State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China 2)Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China
Yang Ding — Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China
Hui Li — Department of Transportation, Southeast University, Nanjing, 210096, China
Tong-Lin Yang — Centre for Molecular Systems and Organic Devices, Key Laboratory for organic Electronics and Information Displays & Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China
Gang-Gui Liu — Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China
Tian-Yun Chu — Jiaxing Tiankun Construction Engineering Design Co., Ltd., Jiaxing 314000, China
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