國立虎尾科技大學 |

Artificial Intelligence-Enabled Self-Healing Infrastructure Systems.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Artificial Intelligence-Enabled Self-Healing Infrastructure Systems./
作者:	McMillan, Lauren.
面頁冊數:	1 online resource (277 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-09, Section: A.
Contained By:	Dissertations Abstracts International85-09A.
標題:	Urban planning. -
電子資源:	click for full text (PQDT)
ISBN:	9798381953145

Artificial Intelligence-Enabled Self-Healing Infrastructure Systems.
McMillan, Lauren.

Artificial Intelligence-Enabled Self-Healing Infrastructure Systems. - 1 online resource (277 pages)

Source: Dissertations Abstracts International, Volume: 85-09, Section: A.

Thesis (Ph.D.)--University of London, University College London (United Kingdom), 2023.

Includes bibliographical references

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 selfhealing 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 realworld systems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381953145Subjects--Topical Terms:

1180826
Urban planning.
Subjects--Index Terms:

Infrastructure systemsIndex Terms--Genre/Form:

554714
Electronic books.

Artificial Intelligence-Enabled Self-Healing Infrastructure Systems.
LDR:03599ntm a22003977 4500 001 1151890
005 20241118135857.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381953145
035 $a (MiAaPQ)AAI30996960
035 $a AAI30996960
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a McMillan, Lauren. $3 1478730
245 1 0 $a Artificial Intelligence-Enabled Self-Healing Infrastructure Systems.
264 0 $c 2023
300 $a 1 online resource (277 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-09, Section: A.
500 $a Advisor: Varga, Liz;Fayaz, Jawad.
502 $a Thesis (Ph.D.)--University of London, University College London (United Kingdom), 2023.
504 $a Includes bibliographical references
520 $a 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 selfhealing 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 realworld systems.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Urban planning. $3 1180826
653 $a Infrastructure systems
653 $a Leakage
653 $a Machine learning
653 $a Self-healing
653 $a Water systems
655 7 $a Electronic books. $2 local $3 554714
690 $a 0543
690 $a 0800
690 $a 0999
710 2 $a University of London, University College London (United Kingdom). $b Science and Technology Studies. $3 1437717
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 85-09A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30996960 $z click for full text (PQDT)