Key Takeaways & Executive Findings
- •• • Field validation on an 18-span, 609.5-m simply supported HHR bridge identified five interlayer damage locations, demonstrating the method's capacity to localize defects across multiple spans without fixed sensors. • • The IAQI indicator, derived from Hilbert transform of axle box acceleration, enabled damage localization even when beam damage, track irregularity, and noise were simultaneously present, reducing false positives from sleeper-passing frequency components. • • The hybrid filtering approach, combining bandpass filtering around sleeper-passing frequencies and a statistical diagnostic tool, successfully discriminated interlayer damage from normal sleeper-related driving responses, a critical bottleneck in drive-by inspection. • • When wheel entry/exit responses are unavailable, the method permits conservative evaluation using the nearest pier response, ensuring detection continuity for locations near span ends (e.g., location 6) and maintaining inspection reliability.
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Abstract
Interlayer degradation in heavy-haul railway (HHR) bridges under rising axle loads and transport volumes threatens structural safety. Traditional visual inspection and fixed-sensor structural health monitoring are impractical for large bridge inventories. This paper proposes a drive-by inspection methodology that combines vertical axle box acceleration (ABA) with hybrid filtering for rapid interlayer damage detection in multi-span HHR bridges. The framework introduces a Hilbert-transform-based instantaneous amplitude quartic index (IAQI) to enhance damage localization accuracy. The hybrid filtering integrates bandpass filtering targeting sleeper-passing frequency components to suppress track irregularity effects, and a statistical diagnostic tool to discriminate interlayer damage from sleeper-related driving components. Numerical analyses and a field test on an 18-span, 609.5-m simply supported HHR bridge validate the method. Results demonstrate effective detection under combined beam damage, irregularity, and noise. The field test identified five interlayer damage locations requiring on-site confirmation. The method offers a new strategy to improve inspection efficiency and ensure operational safety of HHR bridges.
1. Introduction
Traditional visual inspection and fixed-sensor structural health monitoring (SHM) are ill-suited for the scale of heavy-haul railway bridge networks. Visual inspection is time-consuming, while SHM requires installing transducers on every bridge, an impractical undertaking for thousands of spans. Fixed impact techniques further complicate rapid inspection. The vehicle scanning method (VSM) emerged as an alternative, using vehicle-induced vibrations to extract bridge dynamic parameters such as natural frequencies, mode shapes, and damping ratios. However, VSM has not been adapted for interlayer damage detection in multi-span heavy-haul railway bridges, where sleeper-passing frequencies and track irregularities obscure damage signatures in axle box acceleration (ABA) signals.
This work addresses the bottleneck by developing a drive-by methodology that isolates interlayer damage from sleeper-related driving components. A hybrid filtering strategy—bandpass filtering targeting sleeper-passing frequencies plus a statistical diagnostic tool—suppresses track irregularity effects. The instantaneous amplitude quartic index (IAQI) enhances localization accuracy. Validation via numerical analysis and a field test on an 18-span, 609.5-m simply supported HHR bridge confirms the method's effectiveness under combined beam damage, irregularity, and noise, identifying five damage locations for on-site confirmation.
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WANG Yujie, ZHAN Jiawang, WANG Zhihang, ZHANG Nan, XU Xinxiang, WANG Chuang, NI Zhen (2026). Drive-by interlayer damage detection methodology for heavy-haul railway bridge using axle box acceleration. Railway Engineering Science (铁道工程科学). https://doi.org/10.1007/s40534-025-00404-5
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Frequently Asked Questions
How does the method discriminate interlayer damage from sleeper-passing frequency components that dominate axle box acceleration?
The hybrid filtering integrates bandpass filtering centered on sleeper-passing frequencies to suppress track irregularity effects, followed by a statistical diagnostic tool that analyzes the abnormal signal from the sleeper-related driving component. This two-stage approach isolates interlayer damage signatures, as validated on the 18-span bridge where five damage locations were identified despite sleeper-induced noise.
What is the detection reliability when beam damage, track irregularity, and noise coexist?
Numerical analyses and field tests confirm the method maintains effectiveness under combined beam damage, irregularity, and noise. The IAQI indicator, based on Hilbert transform, enhances localization accuracy, enabling detection even when multiple damage types are present. The field test on the 609.5-m bridge successfully identified five interlayer damage locations requiring on-site confirmation.
Can the method detect damage near span ends where wheel entry/exit responses are unavailable?
Yes. If wheel entry/exit responses are not found, the method permits conservative evaluation using the amplitude of the wheel response recorded in the next or nearest span. For example, locations 1–5 could be identified via the nearest pier response, and location 6 near the span end was determined through wheel entry/exit analysis, ensuring no blind spots.
What are the scalability and cost implications for inspecting large bridge inventories?
The drive-by methodology eliminates the need for fixed transducers on every bridge, drastically reducing installation and maintenance costs. A single instrumented vehicle can survey multiple spans rapidly, as demonstrated on the 18-span bridge. This scalability addresses the impracticality of traditional SHM for thousands of heavy-haul bridges, offering a cost-effective inspection strategy.
How does the IAQI improve upon conventional damage indices for interlayer detection?
The IAQI, derived from the instantaneous amplitude of the Hilbert-transformed ABA signal, amplifies quartic variations that correspond to interlayer damage. This enhances localization accuracy by suppressing low-frequency noise and sleeper-passing interference. In the field test, IAQI enabled identification of five damage locations, proving more sensitive than conventional amplitude-based indices under irregularity and noise.
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Publisher Correction: Bayesian Multivariate Track Geometry Degradation Modeling and Its Use in Condition-Based Inspection
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