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Computers and Concrete   Volume 20, Number 5, November 2017, pages 563-572
DOI: http://dx.doi.org/10.12989/cac.2017.20.5.563
 
A systematic method from influence line identification to damage detection: Application to RC bridges
Zhiwei Chen, Weibiao Yang, Jun Li, Qifeng Cheng and Qinlin Cai

 
Abstract     [Buy Article]
    Ordinary reinforced concrete (RC) and prestressed concrete bridges are two popular and typical types of short- and medium-span bridges that accounts for the vast majority of all existing bridges. The cost of maintaining, repairing or replacing degraded existing RC bridges is immense. Detecting the abnormality of RC bridges at an early stage and taking the protective measures in advance are effective ways to improve maintenance practices and reduce the maintenance cost. This study proposes a systematic method from influence line (IL) identification to damage detection with applications to RC bridges. An IL identification method which integrates the cubic B-spline function with Tikhonov regularization is first proposed based on the vehicle information and the corresponding moving vehicle induced bridge response time history. Subsequently, IL change is defined as a damage index for bridge damage detection, and information fusion technique that synthesizes ILs of multiple locations/sensors is used to improve the efficiency and accuracy of damage localization. Finally, the feasibility of the proposed systematic method is verified through experimental tests on a three-span continuous RC beam. The comparison suggests that the identified ILs can well match with the baseline ILs, and it demonstrates that the proposed IL identification method has a high accuracy and a great potential in engineering applications. Results in this case indicate that deflection ILs are superior than strain ILs for damage detection of RC beams, and the performance of damage localization can be significantly improved with the information fusion of multiple ILs.
 
Key Words
    influence line; damage detection; RC bridge; inverse problem; information fusion; experimental verification
 
Address
Zhiwei Chen, Weibiao Yang and Qinlin Cai: Department of Civil Engineering, Xiamen University, Xiamen, China
Jun Li: Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, Kent Street, Bentley, WA 6102, Australia
Qifeng Cheng:
1) Holsin Engineering Testing Co., LTD, Xiamen, China
2) College of Civil Engineering, Huaqiao University, Xiamen, China
 

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