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Lookup NU author(s): Dr Manuel HerreraORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Effective prediction of infrastructure performance is essential for informed asset management. However, traditional approaches often treat different types of assets in isolation, overlooking critical interdependencies (such as those between track and drainage systems) that significantly influence asset degradation and risk. This paper proposes a hybrid model, BaGTA, that is temporally aware, spatially informed and probabilistically grounded to predict railway track performance while accounting for both uncertainty and inter-asset dependencies. The model was trained and validated on a dataset comprising 6,072 track segments and 31,628 drainage assets across four UK railway routes. We demonstrate that incorporating track-drainage interdependencies improves prediction accuracy in both classification and regression tasks. Specifically, the inclusion of interdependencies reduced the prediction error for the Vertical Settlement Standard Deviation (VSD), which is a key indicator of track performance, by 24.65%. The proposed method not only captures complex spatiotemporal relationships but also quantifies uncertainty in predictions, offering a robust decision-support tool for infrastructure operators. This approach has the potential to transform maintenance strategies by enabling proactive, risk-informed, and cost-effective asset management.
Author(s): Pan N, Sasidharan M, Okazaki S, Herrera M, Parlikad AK
Publication type: Article
Publication status: Published
Journal: Reliability Engineering & System Safety
Year: 2025
Pages: epub ahead of print
Online publication date: 25/11/2025
Acceptance date: 22/11/2025
Date deposited: 26/11/2025
ISSN (print): 0951-8320
ISSN (electronic): 1879-0836
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.ress.2025.112019
DOI: 10.1016/j.ress.2025.112019
Data Access Statement: The authors do not have permission to share data.
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