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IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry

Gbadamosi, Abdul; Oyedele, Lukumon; Davila Delgado, Juan Manuel; Kusimo, Habeeb; Akanbi, Lukman; Olawale, Oladimeji; Muhammed-Yakubu, Naimah


Abdul Gbadamosi

Manuel Davila Delgado
Associate Professor - AR/VR Development with Artificial Intelligence

Habeeb Kusimo
Research Associate - Digital Construction with Big Data

Dr Lukman Akanbi
Associate Professor - Big Data Application Developer

Mr Oladimeji Olawale
Research Associate - Project Reputation using Digital Technologies


With about 100% increase in rail service usage over the last 20 years, it is pertinent that rail infrastructure continues to function at an optimal level to avoid service disruptions, cancellations or delays due to unforeseen asset breakdown. In an endeavour to propose a strategy for the implementation of Internet of Things (IoT) in rail asset maintenance, a qualitative methodology was adopted through a series of focus-group workshops to identify the priority areas and enabling digital technologies for IoT implementation. The methods of data collection included audio recording, note- taking, and concept mapping. The audio records were transcribed and used for thematic analysis, while the concept maps were integrated for conceptual modelling and analysis. This paper presents an implementation strategy for IoT for rail assets maintenance with focus on priority areas such as real-time condition monitoring using IoT sensors, predictive maintenance, remote inspection, and integrated asset data management platform.


Gbadamosi, A., Oyedele, L., Davila Delgado, J. M., Kusimo, H., Akanbi, L., Olawale, O., & Muhammed-Yakubu, N. (2021). IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry. Automation in Construction, 122, Article 103486

Journal Article Type Article
Acceptance Date Nov 18, 2020
Online Publication Date Dec 4, 2020
Publication Date Feb 1, 2021
Deposit Date Jan 27, 2021
Publicly Available Date Dec 5, 2021
Print ISSN 0926-5805
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 122
Article Number 103486
Public URL


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