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This paper addresses the temperature-induced variations of measured modal frequencies of steel box girder for a suspension bridge using long-tem monitoring data. The output-only modal frequency identification of the bridge is effectively carried out using the Iterative Windowed Curve-fitting Method (IWCM) in the frequency-domain. The daily and seasonal correlations of frequency-temperature are investigated in detail and the analysis results reveal that: (i) the identified modal frequencies using IWCM provide an effective indication for changes of the bridge due to the ambient temperature variations; (ii) temperature is the critical source causing modal variability, and there is an overall decrease in modal frequency with temperature for all the identified modes; (iii) the random variations in measured modal frequencies mainly arise from the identification algorithm due to the nonstationary loadings, which can be effectively eliminated using multi-sample averaging technique; (iv) the daily averaged modal frequencies of vibration modes have remarkable seasonal correlations with the daily averaged temperature and the seasonal correlation models of frequency-temperature are suitable for structural damage warning if future seasonal correlation models deviate from these normal models.

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This paper focuses on developing an online structural condition assessment technique using long-term monitoring data measured by a structural health monitoring system. The seasonal correlations of frequency-temperature and beam-end displacement-temperature for the Runyang Suspension Bridge are performed, fi rst. Then, a statistical modeling technique using a six-order polynomial is further applied to formulate the correlations of frequency-temperature and displacement-temperature, from which abnormal changes of measured frequencies and displacements are detected using the mean value control chart. Analysis results show that modal frequencies of higher vibration modes and displacements have remarkable seasonal correlations with the environmental temperature and the proposed method exhibits a good capability for detecting the micro damage-induced changes of modal frequencies and displacements. The results demonstrate that the proposed method can effectively eliminate temperature complications from frequency and displacement time series and is well suited for online condition monitoring of long-span suspension bridges.