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Abstract
As critical component of heavy-haul railway (HHR) bridge, the interlayer is designed to transfer vehicle load and dissipate residual oscillation. The increasing transport volumes and axle loads can degrade interlayer condition, thereby threatening the operation safety of HHR bridge. This paper proposes an innovative drive-by inspection methodology combining vertical axle box acceleration with a hybrid filtering approach for rapid interlayer damage detection in multi-span HHR bridges. The developed framework introduces a Hilbert-transform-based indicator, instantaneous amplitude quartic index (IAQI), to enhance damage localization accuracy. The hybrid filtering methodology integrates two components: (1) bandpass filtering targeting sleeper-passing frequency components to suppress track irregularity effects and enhance the interlayer damage detection efficiency; (2) a statistical diagnostic tool to diagnose whether the abnormal signal from the sleeper-related driving component of axle box acceleration is interlayer damage in multi-span HHR bridges. The feasibility of the proposed method is validated by numerical analyses and a field test of an 18-span HHR bridge. The analyses results indicate that the proposed method has good effectiveness and efficiency in detecting interlayer damage, even combined with beam damage, irregularity and noise. This research offers a new strategy to enhance the inspection efficiency and ensure the operation safety of HHR bridges.
Keywords
Heavy-haul railway (HHR) bridge
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Drive-by interlayer damage detection
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Instantaneous amplitude quartic index (IAQI)
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Hybrid filtering analysis
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Axle box acceleration (ABA)
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Yujie Wang, Jiawang Zhan, Zhihang Wang, Nan Zhang, Xinxiang Xu, Chuang Wang, Zhen Ni.
Drive-by interlayer damage detection methodology for heavy-haul Railway Bridge using axle box acceleration.
Railway Engineering Science 1-16 DOI:10.1007/s40534-025-00404-5
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Funding
National Natural Science Foundation of China(52578157)
China State Railway Group Science and Technology Research and Development Program(L2024G002)
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