Structural Health Monitoring with an Active Data Management System for Secondary Road Bridges


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Title: Structural Health Monitoring with an Active Data Management System for Secondary Road Bridges

Author(s): Yoon-Si Lee, Brent Phares, Terry Wipf and Faris Malhas

Publication: Special Publication

Volume: 292


Appears on pages(s): 1-14

Keywords: Data reduction, Field monitoring, Strain, Structural health monitoring

Date: 10/2/2013

This paper presents an autonomous SHM system that was developed to detect and identify overload occurrence, and changes in structural behavior for bridges primarily on the secondary road system. SHM has gained much attention over the past 10 years. However, for the most part the primary focus has been on deployments on Interstate and other primary highway bridges. It is possible, however, for local systems engineers to reap similar benefits as long as cost, scope, and required staff technical abilities fit within local systems restraints. The SHM system utilizes a new approach to identifying and extracting useful information from large data files. By reducing the large data files into smaller packets of the most relevant information, data processing is greatly relieved, reliable analytical results are quickly achieved, and the long-term structural performance of secondary road system bridges can be presented to owners in a clear format that is more easily understood and utilized. Appropriate data processing and evaluation procedures allows the amount of saved data to be significantly reduced to less than 0.1% of collected data and for the data to be “comfortable” to use by local systems engineers. In addition, this system showcases application and testing of traditional strain gage sensors, installation of the system components, and wireless communication from the bridge site to the owner for monitoring updates. The installation of the strain gages and cabling required no training or special equipment other than safety and normal access equipment. Excluding the communication and power equipment and research and development costs, the system can be implemented at the cost of $10,000 to $15,000 depending on the number of sensors used.