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New Statistical Pattern Recognition Technology for Condition Assessment of Cable-stayed Bridge on Earthquake Load
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 Title & Authors
New Statistical Pattern Recognition Technology for Condition Assessment of Cable-stayed Bridge on Earthquake Load
Heo, Gwanghee; Kim, Chunggil;
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 Abstract
In spite of its usefulness for health monitoring of structures on steady external load, the statistical pattern recognition technology (SPRT), based on Mahalanobis distance theory (MDT), is not good enough for the health monitoring of structures on large variability external load like earthquake. Damage is usually determined by the difference between the average measured value of undamaged structure and the measure value of damaged one. So when external variability gets larger, the difference gets bigger along, which is thus easily mistaken for a damage. This paper aims to overcome the problem and develop an improved Mahalanobis distance theory (IMDT), that is, a SPRT with revised MDT in order to decrease external variability so that we will be able to continue to monitor the structure on uncertain external variability. This method is experimentally tested to see if it precisely evaluates the health of a cable-stayed bridge on each general random load and earthquake load. As a result, the IMDT is found to be valid in locating structural damage made by damaged cables by means of data from undamaged cables. So it is proved to be effectively applicable to the health monitoring of bridges on external load of variability.
 Keywords
Statistical pattern recognition technology;Control chart;Mahalanobis distance;Improved mahalanobis distance;Structural health monitoring;
 Language
Korean
 Cited by
 References
1.
Anne, S., Kiremidjian, G. K. and Pooya, S. (2011). "A wireless structural monitoring system with embedded damage algorithms and decision support system." Structure and Infrastructure Engineering, Vol. 7, No. 12, pp. 881-894. crossref(new window)

2.
Charles, R. F. and Hoon, S. (2000). "Pattern recognition for structural health monitoring." Workshop on Mitigation of Earthquake Disaster by Advanced Technologies, Las Vegas, NV, USA, LA-UR-00-5565.

3.
De Lautour, O. R. and Omenzetter, P. (2008). "Visualisation and classification of dynamic structural health monitoring data for assessment of structural condition." 2008 NZSEE Conference, p. 3.

4.
Heo, G. and Kim, C. (2012). "Designing a unified wireless system for vibration control." Soil Dynamics and Earthquake Engineering, Vol. 38, pp. 72-80. crossref(new window)

5.
Hong, D. S. and Kim, J. T. (2010). "Structural health monitoring of full-scale concrete girder bridge using acceleration response." Journal of the Korea Institute for Structural Maintenance Inspection, Vol. 14, No. 1, pp. 165-174 (in Korean).

6.
Kim, H. S. (2011). Bridge health monitoring through signal-based data analysis, Master Dissertation, Inha University, Incheon, Korea.

7.
Mahalanobis, P. C. (1930). "On tests and measures of group divergence." Journal of the Asiatic Society of Bengal, Vol. 26, pp. 541-588.

8.
Nair, K. K. and Kiremidjian, A. S. (2007). "Time series-based structural damage detection algorithm using gaussian mixtures modeling." Journal of Dynamic Systems, Measurement, and Control, Vol. 129, pp. 129-293.

9.
Ruotolo, R. and Surace, C. (1997). "Damage detection using singular value decomposition." Proceedings of DAMAS '97, University of Sheffield, UK, pp. 87-96.

10.
Sohn, H., Farrar, C. F., Hemez, F. M., Shunk, D. D., Stinemates, D. W., Nadler, B. R. and Czarnecki, J. J. (2004). A review of structural health monitoring literature: 1996-2001, Report LA-13976-MS, Los Alamos National Laboratory, Los Alamos, NM, USA.

11.
Wang, Z. and Ong, K. C. G. (2009). "Structural damage detection using autoregressive-model-incorporating multivariate exponentially weighted moving average control chart." Eng Struct, Vol. 31, No. 5, pp. 1265-1275. crossref(new window)

12.
Worden, K., Manson, G. and N. R. J. (2000). "Damage detection using outlier analysis." Journal of Sound and Vibration, Vol. 229, No. 3, pp. 647-667. crossref(new window)