Data underlying the publication: WaveletInception Networks for Drive-by Vibration-Based Infrastructure Health Monitoring

The dataset is an Axle-Box Acceleration simulation dataset based on the vehicle-track interaction model developed by Dr. Chen Shen, as described in the following reference. The rail and sleepers are meshed using Timoshenko beam elements, while the ballast and railpads are represented as discrete spring-damper pairs.Clamps and bolts are not explicitly modeled; instead, their stiffness is incorporated into the railpad stiffness, a widely accepted simplification in railway track modeling. The wheel is simplified as a rigid mass, and the wheel-rail contact is modeled using a Hertzian spring. <br>ABA measurements are simulated at four operational speeds of 40, 50, 55, and 65 km/h, considering floating stiffness reduction. This is the raw dataset, and additive white Gaussian noise scenarios are generated as the paper explains. For details on the dataset, please refer to the cited papers.<br>If you use this dataset, ensure proper citation of the following references.<br>*Shen, C., P. Zhang, R. Dollevoet, A. Zoeteman, and Z. Li, Evaluating Railway Track Stiffness Using Axle Box Accelerations: A Digital Twin Approach. Mechanical Systems and Signal Processing, vol.21 204, 2023, p. 110730.22.<br>*Chen Shen, Rolf Dollevoet, Zili Li, Fast and robust identification of railway track stiffness from simple field measurement, Mechanical Systems and Signal Processing,Volume 152, 2021, 107431.<br>*R. R. Samani, A. Nunez, and B. De Schutter. A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration response. Dec. 3, 2024. doi: 10 . 48550 / arXiv.2412.02643. arXiv: 2412.02643 [cs]. Pre-published.