Temperature gradient of ballastless track in large daily temperature difference region and its influence on dynamic responses of vehicle-track-bridge system

Sun, Zhen; Lou, Ping; Huang, Xinde

doi:10.1016/j.aej.2023.11.027

Cited by 5 publications

(1 citation statement)

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“…Temperature across the same bridge section is correlated; however, temperature gradients within the bridge section will be led by the uneven sunlight and material properties. The static and dynamic characteristics of bridge structures will be affected by the temperature gradients [ 23 ]. Analyzing and extracting temperature gradients is crucial for accurately modeling the TID.…”

Section: Introductionmentioning

confidence: 99%

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

Jiang,

Yang,

Liu

et al. 2024

Sensors

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Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.

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Section: Introductionmentioning

confidence: 99%