A systematic method from influence line identification to damage detection of bridge structures
The number of highway bridges in China has exceeded 805,000 by the end of 2016,and 90%of them are short-and medium-span bridges which accounts for the vast majority.Detecting the abnormality of 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 bridge structures.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.
influence line damage detection bridge inverse problem information fusion experimental verification
Z.W.Chen M.Q.Liu W.B.Yang
Department of Civil Engineering,Xiamen University,Xiamen,China;Xiamen Engineering Technology Center Department of Civil Engineering,Xiamen University,Xiamen,China
国际会议
The 7th World Conference on Structural Control and Monitoring(7WCSCM)(第七届结构控制与监测世界大会)
青岛
英文
307-322
2018-07-22（万方平台首次上网日期，不代表论文的发表时间）