Research data supporting chapter 'A Hybrid Neural Model Approach for Health Assessment of Transition Zones with Multiple Data' of dissertation 'AI Solutions for Maintenance Decision Support in Railway Infrastructure'

The data and codes were prepared and uploaded to 4TU.ResearchData by Wassamon Phusakulkajorn to support the results in Chapter 5 (A Hybrid Neural Model Approach for Health Assessment of Transition Zones with Multiple Data) of her dissertation. This chapter has been submitted for publication as Phusakulkajorn, W., Unsiwilai, S., Chang, L., Núñez, A., Li, Z., A Hybrid Neural Model Approach for Health Assessment of Railway Transition Zones with Multiple Data Sources. In this research, we develop a framework that enables a more frequent evaluation of transition zone health by integrating multiple monitoring technologies, including track geometry measurements, interferometric synthetic aperture radar (InSAR), and axle box acceleration (ABA). This aims to improve an early detection capability for track irregularities. The data used in this research contain ABA, track geometry, InSAR measurements at transitions zone collected from a railway bridge between Dordrecht and Lage Zwaluwe station in the Netherlands. All implementations are done in MATLAB, where (.mat) files are analytical solutions and (.eps) and (.jpg) are figures used in the main manuscript.