Damage detection of a cable-stayed bridge based on the variation of stay cable forces eliminating environmental temperature effects

Based on a recent work by the authors to separate the daily and long-term components of the effective temperature variation and cable force variation, this study first determines two transfer coefficients for these two components of effective temperature variation to eliminate the environmental temperature effects from the cable force variation. Several thresholds corresponding to different levels of exceedance probability are then obtained to establish four upper criteria and four lower criteria for damage detection. With these criteria and the monitoring data for three stay cables of Ai-Lan Bridge, an effective methodology for the detection of instant damages occurred in cable-stayed bridges is developed and verified. The simulated results unambiguously indicate that the damages with cable force changes larger than ±1% can be detected.

[1]  Hong-Zeng Tseng,et al.  Measurement of ambient vibration signal of shorter stay cables from stressing to service stages , 2008 .

[2]  Yi-Qing Ni,et al.  Generalization Capability of Neural Network Models for Temperature-Frequency Correlation Using Monitoring Data , 2009 .

[3]  Zhao-Dong Xu,et al.  Simulation of the Effect of Temperature Variation on Damage Detection in a Long-span Cable-stayed Bridge , 2007 .

[4]  Youliang Ding,et al.  Structural Damage Warning of a Long-Span Cable-Stayed Bridge Using Novelty Detection Technique Based on Wavelet Packet Analysis , 2010 .

[5]  Ming L. Wang,et al.  Temperature effects on cable stayed bridge using health monitoring system: a case study , 2011 .

[6]  Yi-Qing Ni,et al.  Constructing input to neural networks for modeling temperature-caused modal variability: Mean temperatures, effective temperatures, and principal components of temperatures , 2010 .

[7]  Temel Türker,et al.  Structural safety assessment of bowstring type RC arch bridges using ambient vibration testing and finite element model calibration , 2014 .

[8]  Jm M. Ko,et al.  Eliminating Temperature Effect in Vibration-Based Structural Damage Detection , 2011 .

[9]  Yadong Li,et al.  Research on Temperature Influences in Cable-Stayed Bridges’ Health Monitoring , 2012 .

[10]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[11]  Geert Lombaert,et al.  Uncertainty quantification in the damage assessment of a cable-stayed bridge by means of fuzzy numbers , 2009 .

[12]  Yi-Qing Ni,et al.  Assessment of Bridge Expansion Joints Using Long-Term Displacement and Temperature Measurement , 2007 .

[13]  Laurent Mevel,et al.  Structural health monitoring with statistical methods during progressive damage test of S101 Bridge , 2014 .

[14]  Matthew J. Whelan,et al.  In-Service Diagnostics of a Highway Bridge from a Progressive Damage Case Study , 2010 .

[15]  Filipe Magalhães,et al.  Recent perspectives in dynamic testing and monitoring of bridges , 2013 .

[16]  Yi-Qing Ni,et al.  Correlating modal properties with temperature using long-term monitoring data and support vector machine technique , 2005 .

[17]  H. F. Zhou,et al.  Modal Flexibility Analysis of Cable‐Stayed Ting Kau Bridge for Damage Identification , 2008, Comput. Aided Civ. Infrastructure Eng..