A probabilistic state assessment framework for damaged heavy-haul railway bridges using vehicle–bridge collaborative monitoring
- 1State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University
- 2School of Civil Engineering, Beijing Jiaotong University
Abstract
In-service heavy-haul railway bridges often operate under damaged conditions, requiring continuous health monitoring to ensure operational safety and facilitate intelligent maintenance. However, conventional assessment methods based on ground monitoring, limited by sparse sensor deployment, cannot fully capture the global bridge state and its evolution, and neglect stochastic track irregularities that induce significant response variability, thereby decreasing assessment reliability. To address these issues, a distribution-driven probabilistic bridge state assessment framework is proposed based on vehicle–bridge collaborative monitoring. The extreme-value distributions of vibration responses from the bogie, wheelset, and key span sections under stochastic track irregularities and varying bridge damage conditions are characterized using the probability density evolution method, revealing a mapping between damage-induced variations in bridge states and the corresponding distribution shifts. Based on this mapping, baseline thresholds for extreme responses are statistically determined under undamaged conditions to define six distinct bridge health levels. The probability for each level is estimated via a weight-adaptive hierarchical probabilistic evaluation model, which fuses probabilities derived from the extreme-value distributions of multi-source vibration response indicators, with weights allocated according to indicator sensitivity to damage. The bridge state is assessed as the one with the highest probability among the six levels. Case studies on scenarios with different damage locations and severities demonstrate that the proposed framework effectively distinguishes the effects of damage on bridge states and traces state evolution as damage progresses, providing reliable and interpretable assessments for bridge maintenance decision-making.
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BibTeX
@article{shi2026probabilistic,
title = {{A probabilistic state assessment framework for damaged heavy-haul railway bridges using vehicle–bridge collaborative monitoring}},
author = {Jiaqi Shi and Hongmei Shi and Jianbo Li and Nan Zhang},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-60777-3},
url = {https://doi.org/10.1038/s41598-026-60777-3},
note = {CC BY},
}
RIS
TY - JOUR
TI - A probabilistic state assessment framework for damaged heavy-haul railway bridges using vehicle–bridge collaborative monitoring
AU - Jiaqi Shi
AU - Hongmei Shi
AU - Jianbo Li
AU - Nan Zhang
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-60777-3
UR - https://doi.org/10.1038/s41598-026-60777-3
AB - In-service heavy-haul railway bridges often operate under damaged conditions, requiring continuous health monitoring to ensure operational safety and facilitate intelligent maintenance. However, conventional assessment methods based on ground monitoring, limited by sparse sensor deployment, cannot fully capture the global bridge state and its evolution, and neglect stochastic track irregularities that induce significant response variability, thereby decreasing assessment reliability. To address these issues, a distribution-driven probabilistic bridge state assessment framework is proposed based on vehicle–bridge collaborative monitoring. The extreme-value distributions of vibration responses from the bogie, wheelset, and key span sections under stochastic track irregularities and varying bridge damage conditions are characterized using the probability density evolution method, revealing a mapping between damage-induced variations in bridge states and the corresponding distribution shifts. Based on this mapping, baseline thresholds for extreme responses are statistically determined under undamaged conditions to define six distinct bridge health levels. The probability for each level is estimated via a weight-adaptive hierarchical probabilistic evaluation model, which fuses probabilities derived from the extreme-value distributions of multi-source vibration response indicators, with weights allocated according to indicator sensitivity to damage. The bridge state is assessed as the one with the highest probability among the six levels. Case studies on scenarios with different damage locations and severities demonstrate that the proposed framework effectively distinguishes the effects of damage on bridge states and traces state evolution as damage progresses, providing reliable and interpretable assessments for bridge maintenance decision-making.
SN - 2045-2322
ER -