Concrete Cracks Monitoring of a Practical Bridge by Using Structural Health Monitoring Technique

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Concrete cracks monitoring of a practical bridge was discussed in order to investigate the practical application of structural health monitoring (SHM) technique. Some detail information of the SHM system of a practical bridge were introduced and some key issues for building this monitoring system was discussed. Strain of bridge beam generated near the concrete cracks was applied to assess the change of width of cracks, and the monitoring data were analyzed. The condition of this practical was evaluated from the point of view of the change trend of concrete cracks.

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June 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] J.P. Ou, H. Li, Structural health monitoring in mainland china: review and future trends, International Journal of Structural Healthy Monitoring, 2010, 9(3), 219-231.

DOI: 10.1177/1475921710365269

Google Scholar

[2] H. Li, J.P. Ou, Structural health monitoring in mainland of china: theories, technologies and applications, Proceedings of the 2nd Asia-Pacific Workshop on Structural Health Monitoring, Dec. 4-7, 2008, 14-22, Melbourne, Australia.

Google Scholar

[3] A. Famili, S. Leourneau, Monitoring of aircraft opening using statistics and machine learning, IEEE International Conference on Tools with AI-99, Chicago, 1999, November 9-11.

Google Scholar

[4] T. Ogilvie, E. Swidenbank, B.W. Hogg, Use of data mining techniques in the performance monitoring and optimization of a thermal power plant, Knowledge Discovery and Data Mining, IEEE Colloquium , 1998, 7.

DOI: 10.1049/ic:19980647

Google Scholar

[5] S. S. Sandhu, R. Kanapady, K. K. Tamma, Damage Prediction and Estmation in Structural Mechanics Based on Data Mining, The 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining/4th Workshop on Mining Scientific Datasets San Francisco, California, USA, 2001, August26-29.

DOI: 10.1145/502512.502515

Google Scholar

[6] A. Lazarevic, R. Kanapady, C. Kamath, K. Tamma, V. Kumar, Localized prediction of multiple target variables using hierarchical clustering, In Proceedings of the IEEE International Conference on Data Mining, Melbourne, 2003, FL.

DOI: 10.1109/icdm.2003.1250913

Google Scholar

[7] Aleksandar Lazarevic, Ramdev kanapady, Chandrika Kamath, Effective localized regression for damage detection in large complex mechanical structures, The 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, 2004, August 22-25.

DOI: 10.1145/1014052.1014103

Google Scholar

[8] H. Li, S.H. Li, J.P. Ou, and H.W. Li, Modal identification of bridges under varying environmental conditions: Temperature and wind effects, Structural Control and Health Monitoring, 2010, 17(5), 495-512.

DOI: 10.1002/stc.319

Google Scholar

[9] H Sohn, M Dzwonczyk, E.G. Straser, A.S. Kiremidjian, Law KH, T. Meng, An experimental study of temperature effect on modal parameters of the Alamosa Canyon bridge, Earthquake Engineering and Structural Dynamics, 1999, 28(8), 879-97.

DOI: 10.1002/(sici)1096-9845(199908)28:8<879::aid-eqe845>3.0.co;2-v

Google Scholar

[10] C.R. Farrar, S.W. Doebling, P.J. Cornwell, E.G. Straser, Variability of modal parameters measured on the Alamosa Canyon bridge, In: Proceedings of the 15th international modal analysis conference, 1997, 257-63.

Google Scholar