McMillan, Lauren;
(2024)
Artificial Intelligence–enabled self-healing infrastructure systems.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Modern infrastructure systems are grappling with increased complexity and interdependence, struggling to predict and manage failures amid factors like population growth, urbanisation, rapid climate change, and economic challenges. While management methods remain fragmented, the rise of digitalisation and artificial intelligence (AI) offers a chance to adapt complex software-based approaches for infrastructure applications. One such approach is 'self-healing,' which anticipates and autonomously responds to system failures. AI's characteristics align well with self-healing concepts, making it a pivotal enabler. However, AI's current status in infrastructure management is unclear and there is a need to explore its application, learning from best practices in various sectors. Hence, this work presents a framework for self-healing infrastructure systems and explores the key components and processes necessary for implementation. Furthermore, in order to explore practical implementation, the framework is applied to leakage management in a water distribution system. Intelligent, data-driven solutions are proposed for each of the processes – anticipation, detection, and restoration – required to manage leakage as a self-healing system and these are trained and tested on a dataset of over 2,000 district metered areas (DMAs) managed by a UK water company. By offering a rapid and cost-efficient method for the identification of potential leakage, the benefits of this approach include enhanced resilience, optimised repair strategies, and improved consumer confidence, fostering sustainable demand-side behaviours. The contribution is a self-healing framework for management of leakage in water distribution systems, which demonstrates strong performance on the historical data provided and has the potential to be adapted to suit other contexts (including other types of infrastructure network). The findings of this research are of value to infrastructure owners and operators, regulators, and researchers, who see the potential in adopting a complex system perspective and recognise the role of AI in effectively applying this perspective to the management of real-world systems.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Artificial Intelligence–enabled self-healing infrastructure systems |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10189017 |
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