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Smart Structures and Systems Volume 14, Number 2, August 2014 , pages 209-224 DOI: https://doi.org/10.12989/sss.2014.14.2.209 |
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Localized reliability analysis on a large-span rigid frame bridge based on monitored strains from the long-term SHM system |
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Zejia Liu, Yinghua Li, Liqun Tang, Yiping Liu, Zhenyu Jiang and Daining Fang
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| Abstract | ||
| With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years\' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy. | ||
| Key Words | ||
| bridges; localized reliability; long-term health monitoring system; strain preprocessing; statistics | ||
| Address | ||
| Zejia Liu, Yinghua Li, Liqun Tang, Yiping Liu, Zhenyu Jiang and Daining Fang:School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, China. 510640 | ||