Structural health monitoring of the Jiangyin Bridge: system upgrade and data analysis

The Jiangyin Bridge is a suspension bridge with a main span of 1385 m over the Yangtze River in Jiangsu Province, China. Being the first bridge with a main span exceeding 1 km in Chinese mainland, it had been instrumented with a structural health monitoring (SHM) system when completed in 1999. After operation for several years, it was found with malfunction in sensors and data acquisition units, and insufficient sensors to provide necessary information for structural health evaluation. This study reports the SHM system upgrade project on the Jiangyin Bridge. Although implementations of SHM system have been reported worldwide, few studies are available on the upgrade of SHM system so far. Recognizing this, the upgrade of original SHM system for the bridge is first discussed in detail. Especially, lessons learned from the original SHM system are applied to the design of upgraded SHM system right away. Then, performance assessment of the bridge, including: (i) characterization of temperature profiles and effects; (ii) recognition of wind characteristics and effects; and (iii) identification of modal properties, is carried out by making use of the long-term monitoring data obtained from the upgraded SHM system. Emphasis is placed on the verification of design assumptions and prediction of bridge behavior or extreme responses. The results may provide the baseline for structural health evaluation.

[1]  Walter H. Dilger,et al.  Extreme values of thermal loading parameters in concrete bridges , 1992 .

[2]  Yi-Qing Ni,et al.  Structural damage alarming using auto-associative neural network technique : exploration of environment-tolerant capacity and setup of alarming threshold , 2011 .

[3]  Chung Bang Yun,et al.  Smart structure technologies for civil infrastructures in Korea: recent research and applications , 2011 .

[4]  Fabrizio Vestroni,et al.  Damage Observability, Localization and Assessment based on Eigenfrequencies and Eigenvectors Curvatures , 2011 .

[5]  Jinping Ou,et al.  Structural Health Monitoring in mainland China: Review and Future Trends , 2010 .

[6]  J M W Brownjohn,et al.  Structural health monitoring of civil infrastructure , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  Yi-Qing Ni,et al.  Variability of measured modal frequencies of a cable-stayed bridge under different wind conditions , 2007 .

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

[9]  Yozo Fujino,et al.  Bridge monitoring in Japan: the needs and strategies , 2011 .

[10]  Mo Shing Cheung,et al.  FIELD MONITORING AND RESEARCH ON PERFORMANCE OF THE CONFEDERATION BRIDGE , 1997 .

[11]  Maria Q. Feng.  Application of structural health monitoring in civil infrastructure , 2009 .

[12]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[13]  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 .

[14]  Yi-Qing Ni,et al.  Modeling of wind and temperature effects on modal frequencies and analysis of relative strength of effect , 2008 .

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