Real-time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Afrazi, M; Jahed Armaghani, D; Afrazi, H; Fattahi, H; Samui, P

Real-time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Afrazi, M Jahed Armaghani, D

Afrazi, H Fattahi, H Samui, P

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: Deep Underground Science and Engineering, 2025
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Afrazi, M
dc.contributor.author	Jahed Armaghani, D https://orcid.org/0000-0001-8171-6403
dc.contributor.author	Afrazi, H
dc.contributor.author	Fattahi, H
dc.contributor.author	Samui, P
dc.date.accessioned	2025-07-25T02:23:41Z
dc.date.available	2025-07-25T02:23:41Z
dc.date.issued	2025-01-01
dc.identifier.citation	Deep Underground Science and Engineering, 2025
dc.identifier.issn	2097-0668
dc.identifier.issn	2770-1328
dc.identifier.uri	http://hdl.handle.net/10453/188829
dc.description.abstract	Tunnels are essential components of contemporary infrastructure, yet guaranteeing their safety, longevity, and efficiency remains a persistent challenge. Recent breakthroughs in artificial intelligence (AI) and digital twin (DT) technology provide innovative solutions for the real-time monitoring of tunnel systems, suggesting proactive maintenance tactics and improved safety protocols. This review paper offers a comprehensive examination of the application of AI and DT methodologies in tunnel surveillance. We explore the core concepts of AI and DT and their applicability to structural monitoring, encompassing machine learning, computer vision, and sensor integration. Through the utilization of these AI-powered technologies, engineers are equipped with unparalleled insights into the state and behavior of tunnels, facilitating the early identification of irregularities and the optimization of maintenance timelines. We discuss the array of AI techniques utilized for the immediate monitoring of tunnel systems, emphasizing their foundations, benefits, and practical uses. Numerous studies have showcased the effectiveness and adaptability of AI-based monitoring systems in various tunnel settings. Moreover, we address the hurdles and constraints inherent in AI and DT methodologies and suggest strategies for overcoming them, such as data augmentation, interpretable AI, edge computing, and continuous monitoring. Ultimately, the incorporation of AI and DT technologies into tunnel surveillance signifies a paradigm shift, offering substantial advantages over conventional techniques. By adopting AI-driven monitoring systems, tunnel operators can augment safety, prolong the lifespan of infrastructure, and decrease operational expenses, molding the future of subterranean infrastructure management.
dc.language	English
dc.publisher	WILEY
dc.relation.ispartof	Deep Underground Science and Engineering
dc.relation.isbasedon	10.1002/dug2.70029
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Real-time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview
dc.type	Journal Article
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-25T02:23:39Z
pubs.publication-status	Published

Abstract:

Tunnels are essential components of contemporary infrastructure, yet guaranteeing their safety, longevity, and efficiency remains a persistent challenge. Recent breakthroughs in artificial intelligence (AI) and digital twin (DT) technology provide innovative solutions for the real-time monitoring of tunnel systems, suggesting proactive maintenance tactics and improved safety protocols. This review paper offers a comprehensive examination of the application of AI and DT methodologies in tunnel surveillance. We explore the core concepts of AI and DT and their applicability to structural monitoring, encompassing machine learning, computer vision, and sensor integration. Through the utilization of these AI-powered technologies, engineers are equipped with unparalleled insights into the state and behavior of tunnels, facilitating the early identification of irregularities and the optimization of maintenance timelines. We discuss the array of AI techniques utilized for the immediate monitoring of tunnel systems, emphasizing their foundations, benefits, and practical uses. Numerous studies have showcased the effectiveness and adaptability of AI-based monitoring systems in various tunnel settings. Moreover, we address the hurdles and constraints inherent in AI and DT methodologies and suggest strategies for overcoming them, such as data augmentation, interpretable AI, edge computing, and continuous monitoring. Ultimately, the incorporation of AI and DT technologies into tunnel surveillance signifies a paradigm shift, offering substantial advantages over conventional techniques. By adopting AI-driven monitoring systems, tunnel operators can augment safety, prolong the lifespan of infrastructure, and decrease operational expenses, molding the future of subterranean infrastructure management.

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http://hdl.handle.net/10453/188829