Indirect Damage Identification Method for Railway Bridges Based on Train Response Sensitivity

For heavy-haul railway bridges that suffer from stiffness degradation and structural damage under long-term high-axle-load service conditions, conventional direct bridge damage identification methods are restricted by the high deployment and maintenance costs of dense sensor systems. To address this problem, this paper proposes an indirect bridge damage identification method based on train response sensitivity. A vehicle-track-bridge coupled dynamic model accounting for track irregularity excitation is established. The sensitivity equation for damage inversion is formulated by taking the pitch angular velocity of the leading vehicle as the key train response metric. Tikhonov regularization is introduced to alleviate the ill-posedness of the inverse problem and is further combined with non-negativity constraints on damage factors and a dynamic locking strategy. On this basis, a joint identification scheme relying on vehicle-bridge interaction is developed by incorporating bridge mid-span acceleration responses into the inversion framework. Three types of numerical scenarios, namely single damage, multiple damage, and minor damage, are designed to compare different inversion strategies and systematically investigate the influences of train speed, measurement noise, and track irregularity level on identification performance. The results demonstrate that physical constraints effectively suppress spurious damage and improve the accuracy of both damage localization and severity quantification. The joint identification method based on vehicle-bridge interaction significantly outperforms the approach using only train response in terms of minor damage detection capability and noise robustness, and can stably locate the primary damage sites within the speed range of 40–80 km/h. This method enables a hierarchical monitoring strategy integrating preliminary onboard screening with refined bridge-based diagnosis, offering a cost-effective technical solution for the structural health monitoring of heavy-haul railway bridges.