Analyzing surface settlement factors in single and twin tunnels: A review study

Huat, CY; Armaghani, DJ; Lai, SH; Motaghedi, H; Asteris, PG; Fakharin, P

Analyzing surface settlement factors in single and twin tunnels: A review study

Huat, CY Armaghani, DJ Lai, SH Motaghedi, H Asteris, PG Fakharin, P

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Engineering Research Kuwait, 2024
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Huat, CY
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Lai, SH
dc.contributor.author	Motaghedi, H
dc.contributor.author	Asteris, PG
dc.contributor.author	Fakharin, P
dc.date.accessioned	2025-07-16T05:13:11Z
dc.date.available	2025-07-16T05:13:11Z
dc.date.issued	2024-01-01
dc.identifier.citation	Journal of Engineering Research Kuwait, 2024
dc.identifier.issn	2307-1877
dc.identifier.issn	2307-1885
dc.identifier.uri	http://hdl.handle.net/10453/188420
dc.description.abstract	Surface settlement (SS) resulting from tunnel excavation operations is a critical concern in tunnel engineering due to its potential impact on adjacent structures. This review synthesizes current knowledge on factors influencing SS induced by tunneling activities, focusing on tunnel geometry, soil properties, and operational parameters. Empirical formulas, numerical analyses, and machine learning (ML) techniques are examined for the effectiveness in predicting SS, highlighting the limitations and potential. Key findings underscore the significant influence of tunnel geometry, soil properties and tunnel operational parameters on SS outcomes. However, limitations exist in current studies, including the lack of consideration for diverse soil types and operational parameters like jack force thrust and penetration rate. The study underscores the importance of proper management of tunneling operations, including optimizing face pressure, to mitigate SS risks. Practical implications for practicing engineers include thorough site investigations, risk assessments and comprehensive monitoring programs. Leveraging historical data and ML algorithms can enhance SS prediction accuracy and aid in proactive risk management. Ultimately, mitigating SS risks is crucial for safeguarding existing infrastructure in congested urban areas.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Engineering Research Kuwait
dc.relation.isbasedon	10.1016/j.jer.2024.05.009
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Analyzing surface settlement factors in single and twin tunnels: A review study
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	2025-07-16T05:13:06Z
pubs.publication-status	Published

Abstract:

Surface settlement (SS) resulting from tunnel excavation operations is a critical concern in tunnel engineering due to its potential impact on adjacent structures. This review synthesizes current knowledge on factors influencing SS induced by tunneling activities, focusing on tunnel geometry, soil properties, and operational parameters. Empirical formulas, numerical analyses, and machine learning (ML) techniques are examined for the effectiveness in predicting SS, highlighting the limitations and potential. Key findings underscore the significant influence of tunnel geometry, soil properties and tunnel operational parameters on SS outcomes. However, limitations exist in current studies, including the lack of consideration for diverse soil types and operational parameters like jack force thrust and penetration rate. The study underscores the importance of proper management of tunneling operations, including optimizing face pressure, to mitigate SS risks. Practical implications for practicing engineers include thorough site investigations, risk assessments and comprehensive monitoring programs. Leveraging historical data and ML algorithms can enhance SS prediction accuracy and aid in proactive risk management. Ultimately, mitigating SS risks is crucial for safeguarding existing infrastructure in congested urban areas.

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http://hdl.handle.net/10453/188420