Mapping temperature contours for a long-span steel truss arch bridge based on field monitoring data

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING(2021)

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摘要
The temperature-induced response of long-span steel bridges can be more significant than the structural responses associated with operational loads or structural damage. These responses depend on the spatio-temporal temperature variation in bridge members, including the effective temperature and temperature difference within members. Bridges are designed to withstand the extreme temperature variations predicted for a given site. Hence, numerous studies have employed statistical analysis techniques to provide critical information for the design and maintenance of bridges during life cycles. However, the correlation between the effective temperature and temperature difference is usually ignored, which can result in inaccurate assessment of the extreme temperatures in members. In this work, the joint probability distribution for the temperature variation in a long-span steel truss arch bridge is investigated based on field monitoring data. The extreme temperature variations are mapped on contours with relevant return periods; the results show that the probability distribution of the effective temperature can be described using the normal distribution; the weighted sum of two lognormal distributions can describe the distribution of temperature difference. Moreover, extreme values of the effective temperature and temperature difference do not occur concurrently. The effective temperature and temperature difference in the structural member directly exposed to solar radiation vary significantly, while the temperature of the shaded member can be assumed uniform, which is mainly affected by air temperature. The study leads to more accurate estimation of the temperature extremes in long-span steel truss arch bridges, which is of great importance for proper design and maintenance of bridges.
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关键词
Temperature,Temperature difference,Long-span steel truss arch bridge,Filed monitoring data,Joint probability distribution,Extreme temperature variation
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