Data underlying the publication: A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response

DOI:10.4121/64600497-7ba5-4831-9c94-1b643f600987.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/64600497-7ba5-4831-9c94-1b643f600987
Datacite citation style:
Riahi Samani, Reza (2025): Data underlying the publication: A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/64600497-7ba5-4831-9c94-1b643f600987.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

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. ABA measurements are simulated at an operational speed of 65 km/h by 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.


If you use this dataset, ensure proper citation of the following references.


*Shen, C., P. Zhang, R. Dollevoet, A. Zoeteman, and Z. Li, Evaluating Railway Track Stiffness Using20 Axle Box Accelerations: A Digital Twin Approach. Mechanical Systems and Signal Processing, vol.21 204, 2023, p. 110730.22


*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.

History

  • 2025-02-28 first online, published, posted

Publisher

4TU.ResearchData

Format

.npy

Organizations

TU Delft Faculty of Mechanical Engineering, Delft Center for Systems and Control

DATA

Files (3)