Smart Structures and Systems

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Temperature distribution analysis of steel box-girder based on long-term monitoring data

  • Wang, Hao (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Zhu, Qingxin (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Zou, Zhongqin (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Xing, Chenxi (School of Law, Southeast University) ;
  • Feng, Dongming (Weidlinger Transportation Practice, Thornton Tomasetti) ;
  • Tao, Tianyou (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University)
  • Received : 2019.08.24
  • Accepted : 2019.12.09
  • Published : 2020.05.25
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Abstract

Temperature may have more significant influences on structural responses than operational loads or structural damage. Therefore, a comprehensive understanding of temperature distributions has great significance for proper design and maintenance of bridges. In this study, the temperature distribution of the steel box girder is systematically investigated based on the structural health monitoring system (SHMS) of the Sutong Cable-stayed Bridge. Specifically, the characteristics of the temperature and temperature difference between different measurement points are studied based on field temperature measurements. Accordingly, the probability density distributions of the temperature and temperature difference are calculated statistically, which are further described by the general formulas. The results indicate that: (1) the temperature and temperature difference exhibit distinct seasonal characteristics and strong periodicity, and the temperature and temperature difference among different measurement points are strongly correlated, respectively; (2) the probability density of the temperature difference distribution presents strong non-Gaussian characteristics; (3) the probability density function of temperature can be described by the weighted sum of four Normal distributions. Meanwhile, the temperature difference can be described by the weighted sum of Weibull distribution and Normal distribution.

Keywords

Acknowledgement

The authors would like to gratefully acknowledge the supports from the National Basic Research Program of China (973 Program) (Grant No. 2015CB060000), the National Natural Science Foundation of China (Grant No. 51722804), the National Ten Thousand Talent Program for Young Top-notch Talents (Grant No. W03070080), the Jiangsu Transportation Scientific Research Project (Grant No. 8505001498), the Jiangsu Key R & D Plan (Grant No. BE2018120), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX19_0095). The authors also thank the help from Jiangsu Transportation Institute (JSTI) for providing long-term monitoring data.

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