Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies

Niu, G; He, X; Xu, H; Dai, S

Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies

Niu, G He, X

Xu, H

Dai, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Natural Hazards Research, 2024, 4, (1), pp. 148-168
Issue Date:: 2024-03-01

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Field	Value	Language
dc.contributor.author	Niu, G
dc.contributor.author	He, X https://orcid.org/0000-0001-5336-3663
dc.contributor.author	Xu, H https://orcid.org/0000-0002-6556-8831
dc.contributor.author	Dai, S https://orcid.org/0000-0001-8543-1448
dc.date.accessioned	2025-01-02T06:47:42Z
dc.date.available	2025-01-02T06:47:42Z
dc.date.issued	2024-03-01
dc.identifier.citation	Natural Hazards Research, 2024, 4, (1), pp. 148-168
dc.identifier.issn	2666-5921
dc.identifier.issn	2666-5921
dc.identifier.uri	http://hdl.handle.net/10453/182850
dc.description.abstract	Shallow tunnels in urban areas are close to adjacent buildings and municipal pipelines. Ground surface settlement (GSS) due to tunnelling can cause damage to those infrastructures surrounded. Many methods have been proposed for evaluating ground settlement induced by tunnelling, including empirical, analytical, numerical and artificial intelligence methods. This paper reviews the proposed methods in detail based on published 677 articles within past ten years. The principles, assumptions and application scope of those methods are summarized and the advantages and limitations of each method are discussed. Since artificial intelligence (AI) become popular in recent few years, the application of AI in the aspect of tunnelling-induced ground deformation is introduced emphatically. Finally, the challenges of ground displacement prediction by machine learning (ML) are clarified and future research directions are suggested.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Natural Hazards Research
dc.relation.isbasedon	10.1016/j.nhres.2023.11.002
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies
dc.type	Journal Article
utslib.citation.volume	4
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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-01-02T06:47:41Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	1

Abstract:

Shallow tunnels in urban areas are close to adjacent buildings and municipal pipelines. Ground surface settlement (GSS) due to tunnelling can cause damage to those infrastructures surrounded. Many methods have been proposed for evaluating ground settlement induced by tunnelling, including empirical, analytical, numerical and artificial intelligence methods. This paper reviews the proposed methods in detail based on published 677 articles within past ten years. The principles, assumptions and application scope of those methods are summarized and the advantages and limitations of each method are discussed. Since artificial intelligence (AI) become popular in recent few years, the application of AI in the aspect of tunnelling-induced ground deformation is introduced emphatically. Finally, the challenges of ground displacement prediction by machine learning (ML) are clarified and future research directions are suggested.

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http://hdl.handle.net/10453/182850