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Closely spaced modes commonly observed in long-span suspension bridges can greatly increase the difficulty of identifying and tracking modal parameters. Most existing studies generally focus on identifying the closely spaced modes and quantifying the uncertainties based on numerical and experimental models. Further research focusing on full-scale long-span bridges is still required. A case study on identifying the closely spaced modes of the Qixiashan Yangtze River Bridge, a long-span suspension bridge with a main span of 1 418 m, is conducted in this paper. The effectiveness of the generalized fast Bayesian fast Fourier transform (GFBFFT) method is verified by both the simulated and monitoring data. The results show that a larger coefficient of variation (COV) and higher uncertainty is typically contained in the closely spaced modes than the separated modes. Compared with the FDD and SSI methods, the GFBFFT method guarantees higher identification accuracy of modal parameters and can serve as a reliable tool to identify the closely spaced modes.

The cables of the long-span bridge are usually featured as ultra-low frequency, hence making the acceleration unable to accurately capture the information, e.g. damping ratios, for assessing the cable state assessment and mitigating the excessive structural vibration. The displacement was approved to be more sensitive to the low-frequency vibration than the acceleration. However, there is still a lack of effective method to accurately monitor the long-term displacements of bridge cables using reference-free methods. To address this issue, this paper develops a novel acceleration-based approach for monitoring the long-term displacements of the cables of long-span bridges. In the monitoring scheme, recursive least squares method is utilized to conduct baseline correction in the time domain integration of acceleration. An adaptive band-pass filtering method considering cable vibration characteristics is used to eliminate noise, thus avoiding the difficulty of selecting the cut-off frequency by experience in traditional methods. A numerical test of an analytical cable model and a field experiment of the hanger of a full-scale suspension bridge are applied to the applicability and robustness of the developed method. Result shows that adaptive band-pass filter considering the vibration characteristics is suitable for estimating the displacements of the cables. The estimated displacements using the developed method agree well with the background truth in both time and frequency domains.

The mapping for temperature-induced strains is a reliable approach to characterize bridge structures. However, the temperature field of bridge structures is intrinsically time-varying due to the time-dependent nature of the solar radiation and surrounding environments. In addition, long-span steel truss arch railway bridges consist of thousands of structural members. Capturing the accurate relationships between bridge temperature and temperature-induced strains is challenging. To explore an accurate mapping for temperature-induced strains in bridges, this study systematically investigates the temperature distributions and temperature effects on a long-span steel truss arch railway bridge based on field monitoring data. Accordingly, the relationships between temperature-induced strains and spatial temperature distributions are investigated using the principal component analysis (PCA) and gated recurrent unit (GRU), which are applied to calculate structural strains under ambient excitation using field temperature measurements. Particularly, the GRUs with different combinations of inputs, including temperature increments, principal components of temperature increments, and the recorded time of the temperature, are trained. The predictions from different GRUs are compared with the field monitoring data. Results show that temperature-induced strains are profoundly affected by the temperature field of the bridge. The temperature-induced strains can be predicted accurately using GRU in cooperation with field temperature measurements from multi-measurement points. Note that the recorded time of the bridge temperature can be employed to represent the characteristics of the temperature field during the prediction. The results of this study can improve the performance assessment methods for bridge structures, which can be utilized for the abnormal detection of bridge structures.