TY - GEN
T1 - Wireless soft elastomeric capacitor sensor network for long-term fatigue crack monitoring of steel bridges
AU - Kong, Xiangxiong
AU - Jeong, Jong Hyun
AU - Asadollahi, Parisa
AU - Fu, Yuguang
AU - Jo, Hongki
AU - Bennett, Caroline
AU - Collins, William
AU - Laflamme, Simon
AU - Li, Jian
N1 - Funding Information:
This work was supported by Transportation Pooled Fund Study TPF-5(328), which includes the following participating state DOTs: Kansas, Iowa, Minnesota, North Carolina, Pennsylvania, Texas, and Oklahoma; and Iowa Department of Transportation grant #RT454-494. Their support is gratefully acknowledged.
Publisher Copyright:
© 2019 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Long-term monitoring of fatigue cracks in large-scale bridge structures poses unique challenges due to the randomness of crack occurrences, small crack openings that impose negligible impact on global structural responses, slow crack propagation, and environmental impact. In this paper, we describe a recently-created integrated wireless fatigue cracking monitoring system with several unique features aimed at addressing these challenges. These features include: 1) a low cost large-size skin-type capacitive strain sensor to cover large bridge surfaces. A network of such sensors is deployed to capture potential crack initiation and propagation over large fatigue susceptible regions, 2) a novel crack growth index derived from the measured capacitance response, which is robust against long-term signal drift due to environmental impact, 3) a dedicated capacitance sensor board to enable robust wireless data acquisition through the next generation wireless sensing platform Xnode, 4) a novel low-power triggering mechanism to enable autonomous and continuous sensing and data acquisition for long-term monitoring. The integrated monitoring system was deployed in a full-scale steel highway bridge and preliminary field data are presented in this paper.
AB - Long-term monitoring of fatigue cracks in large-scale bridge structures poses unique challenges due to the randomness of crack occurrences, small crack openings that impose negligible impact on global structural responses, slow crack propagation, and environmental impact. In this paper, we describe a recently-created integrated wireless fatigue cracking monitoring system with several unique features aimed at addressing these challenges. These features include: 1) a low cost large-size skin-type capacitive strain sensor to cover large bridge surfaces. A network of such sensors is deployed to capture potential crack initiation and propagation over large fatigue susceptible regions, 2) a novel crack growth index derived from the measured capacitance response, which is robust against long-term signal drift due to environmental impact, 3) a dedicated capacitance sensor board to enable robust wireless data acquisition through the next generation wireless sensing platform Xnode, 4) a novel low-power triggering mechanism to enable autonomous and continuous sensing and data acquisition for long-term monitoring. The integrated monitoring system was deployed in a full-scale steel highway bridge and preliminary field data are presented in this paper.
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M3 - Conference contribution
AN - SCOPUS:85091418907
T3 - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings
SP - 570
EP - 575
BT - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure
A2 - Chen, Genda
A2 - Alampalli, Sreenivas
PB - International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII
T2 - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019
Y2 - 4 August 2019 through 7 August 2019
ER -