Railroad bridge monitoring using wireless smart sensors
- Authors
- Moreu, Fernando; Kim, Robin Eunju; Spencer, Billie F
- Issue Date
- Feb-2017
- Publisher
- WILEY-BLACKWELL
- Keywords
- autonomous monitoring; live load estimation; railroad bridge management; strain; structural health monitoring; wireless smart sensors
- Citation
- STRUCTURAL CONTROL & HEALTH MONITORING, v.24, no.2, pp.1 - 17
- Indexed
- SCIE
SCOPUS
- Journal Title
- STRUCTURAL CONTROL & HEALTH MONITORING
- Volume
- 24
- Number
- 2
- Start Page
- 1
- End Page
- 17
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/20555
- DOI
- 10.1002/stc.1863
- ISSN
- 1545-2255
- Abstract
- Railroads carry more than 40% of the freight, in terms of tons per mile transported in North America. A critical portion of the railroad infrastructure is the more than 100,000 bridges, which occur, on the average, every 1.4 miles of track. Railroads have a limited budget for capital investment. Therefore, decisions on which bridges to repair/replace become critical for both safety and economy. North American railroads regularly inspected bridges to ensure safe operation that can meet transport demands, using inspection reports to decide which bridges may need maintenance, replacement, or further investigation. Current bridge inspection practices recommend observing bridge responses under live load to help assess bridge condition. However, measuring bridge responses under train loads in the field is a challenging, expensive, and complex task. This research explores the potential of using wireless smart sensors (WSS) to measure bridge responses under revenue service traffic that can be used to inform bridge management decisions. Wireless strain gages installed on the rail measure real-time train loads. Wireless accelerometers and magnetic strain gages installed in the bridge measure associated bridge responses. The system is deployed and validated on a double-track steel truss bridge on the south side of Chicago, Illinois, owned by the Canadian National Railway. A calibrated finite element model of the bridge with known train input load estimated the responses of the bridge at arbitrary, unmeasured locations, showing the possibility of applying the system for decision making process. These results demonstrate the potential of WSS technology to assist with railroad bridge inspection and management practice.
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