Title

Computer Vision For Structural Health Monitoring And Damage Detection Of Bridges

Abstract

Structural performance of Civil Infrastructure Systems (CIS) often decreases due to reasons such as damage, over loading, severe environmental conditions, and aging due to normal continued use. These effects will result in long-term structural damage and deterioration. As a result, novel Structural Health Monitoring (SHM) strategies are increasingly becoming more important. In this paper, integrated use of video images and sensor data in the context of SHM is demonstrated as promising technologies for safety and security of bridges. The synchronized image and sensing data are analyzed to obtain Unit Influence Line (UIL) as an index for monitoring bridge behavior under identified loading conditions. The UCF 4-span bridge model is used to explore the use of imaging devices and traditional sensing technology with UIL for damage detection. Different damage scenarios such as changes in boundary conditions and loss of connectivity between composite sections are analyzed. Experimental data is processed by means of statistical methods. Outlier detection algorithms are used to identify structural changes in large data sets obtained by monitoring and results are presented. Finally, advantages and disadvantages of the method are discussed. ©2010 Society for Experimental Mechanics Inc.

Publication Date

1-1-2011

Publication Title

Conference Proceedings of the Society for Experimental Mechanics Series

Volume

5

Number of Pages

125-135

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-1-4419-9825-5_13

Socpus ID

79960316773 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79960316773

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