Void damage identification in post-installed rebar reinforced slab tracks via impact hammer tests and deep learning

Slab track systems have been extensively adopted in high-speed railways worldwide. Post-installed reinforcement bars are commonly employed at the interface between precast slabs and underlying concrete structures to mitigate thermal arching risks. However, field inspections have revealed localized void damage in slab tracks reinforced by post-installed reinforcement bars. This type of damage is often concealed, making identification and diagnosis challenging. Traditional detection methods are costly and entail complex operational procedures. This study presents a novel rapid identification model for void damage assessment in post-installed rebar-reinforced slab tracks, integrating impact hammer testing with deep learning algorithms. To systematically validate the model's feasibility and reliability, laboratory experiments were conducted to simulate varying degrees of post-repair void damage scenarios. Time-frequency analysis was performed to extract latent features from vibration acceleration profiles across different damage conditions, followed by damage diagnosis using specifically designed neural network architectures. The results show that the proposed model can achieve high-precision identification of void damage based on measured structural acceleration, with diagnostic accuracy exceeding 98 % when using one-dimensional convolutional neural networks and residual neural networks. The study represents the first successful identification of void damage in slab tracks post-repair with post-installed reinforcement bars, enhancing the assessment and diagnosis of track system service conditions and effectively guiding practical engineering maintenance. • Innovative Experimentation: Simulated void damage in PIR-reinforced slab tracks via hammering to capture vibration signals. • Advanced Analysis: Used time-frequency analysis to extract features from signals for void damage level identification. • High-Precision Detection: Developed deep learning networks for 98%-accurate void damage recognition in PIR repairs.