Civil Engineering Faculty Publications
Document Type
Article
Publication Date
8-20-2026
Abstract
Aging steel railway bridges accumulate fatigue damage at locations that periodic inspection rarely reaches and that conventional vibration monitoring detects only after severe stiffness loss. Existing physics-informed learning methods for structural health monitoring are mostly validated on synthetic data and single snapshots, leaving their performance on continuous field data with verified damage untested. This study develops a strain-based monitoring approach, supported by a physics-informed neural network (PINN), for early detection of fatigue damage in a steel railway bridge. The measured strain is compensated for temperature using a model calibrated on healthy-state data, the resulting anomaly is tracked through a statistical control chart, and a PINN embedding the Euler–Bernoulli equation and the strain–stiffness relationship converts the sparse measurements into a continuous, physically valid flexural-stiffness field, identified through two-stage training. The framework was evaluated on 56 ambient recordings spanning 19 days across an inspection-confirmed fatigue crack in the Vänersborg railway bridge, Sweden, using only three strain gauges (SG9, SG10, SG11) and one accelerometer (A5). The main findings are: the framework issued a warning 15 days before the in-service monitoring system; the identified stiffness reduction concentrated in the region of the crack-adjacent gauge, consistent with the inspection finding; the identified stiffness field tracked the progression of the damage as a normalized reduction relative to the healthy baseline; and perfect detection was achieved under a persistence rule with zero false alarms. A co-located accelerometer recorded a 94-fold spike at crack propagation but gave no prior warning, confirming that the early detection derives from the temperature-corrected strain identification. The framework provides a reproducible, low-cost, and interpretable basis for proactive bridge maintenance.
Recommended Citation
Khan, Arslan Qayyum, Ali Raza, and Amorn Pimanmas. "Physics-informed neural networks for early-warning fatigue damage detection and localization in a steel railway bridge using sparse ambient monitoring data." Intelligent Transportation Infrastructure (2026): liag010. https://doi.org/10.1093/iti/liag010
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Intelligent Transportation Infrastructure
DOI
10.1093/iti/liag010
Comments
© The Author(s) 2026. Published by Oxford University Press and Southwest Jiaotong University. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.