Blast loading prediction from methane-air explosion in long straight tunnels

Chen, D; Zhang, H; Li, J; Wu, C

Blast loading prediction from methane-air explosion in long straight tunnels

Chen, D Zhang, H Li, J

Wu, C

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Tunnelling and Underground Space Technology, 2025, 165, pp. 106834
Issue Date:: 2025-11-01

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Field	Value	Language
dc.contributor.author	Chen, D
dc.contributor.author	Zhang, H
dc.contributor.author	Li, J https://orcid.org/0000-0003-2457-1994
dc.contributor.author	Wu, C https://orcid.org/0000-0001-8907-8493
dc.date.accessioned	2026-01-08T23:50:03Z
dc.date.available	2026-01-08T23:50:03Z
dc.date.issued	2025-11-01
dc.identifier.citation	Tunnelling and Underground Space Technology, 2025, 165, pp. 106834
dc.identifier.issn	0886-7798
dc.identifier.uri	http://hdl.handle.net/10453/191555
dc.description.abstract	Long, straight underground tunnels are vital to modern infrastructure, including resource extraction, transportation, and hydropower, but their confined nature heightens the risks posed by methane-air explosions. This study presents a dimensionless predictive model for evaluating blast loading resulting from methane-air explosions in long, straight tunnels, integrating numerical simulations, parametric analyses, and an artificial neural network (ANN)-based approach. By adopting dimensionless input and output parameters, the model becomes inherently scalable to various tunnel sizes and configurations, eliminating the influence of scale effects and ensuring a broad range of applicability. A validated CFD model using FLACS provided the database for the ANN training, capturing the transition from deflagration to shock wave formation as well as key pressure duration and magnitude characteristics. The ANN-based model, accounting for cross-sectional area and shape, tunnel length, fuel length, blockage ratio and obstacle spacing, demonstrated acceptable predictive capability with R<sup>2</sup> values above 0.95 and most predictions within a ± 30 % error margin. Validation against large-scale experiments confirmed its reliability and practicality. This model significantly reduces computational costs and time, offering an efficient tool for predicting methane-air explosion loads in underground tunnels.
dc.language	en
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/DP210101100
dc.relation.ispartof	Tunnelling and Underground Space Technology
dc.relation.isbasedon	10.1016/j.tust.2025.106834
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 0914 Resources Engineering and Extractive Metallurgy
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4019 Resources engineering and extractive metallurgy
dc.title	Blast loading prediction from methane-air explosion in long straight tunnels
dc.type	Journal Article
utslib.citation.volume	165
utslib.for	0905 Civil Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
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/Centre for Built Infrastructure Resilience (CBIR)
utslib.copyright.status	open_access	*
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	2026-01-08T23:50:01Z
pubs.publication-status	Published
pubs.volume	165

Abstract:

Long, straight underground tunnels are vital to modern infrastructure, including resource extraction, transportation, and hydropower, but their confined nature heightens the risks posed by methane-air explosions. This study presents a dimensionless predictive model for evaluating blast loading resulting from methane-air explosions in long, straight tunnels, integrating numerical simulations, parametric analyses, and an artificial neural network (ANN)-based approach. By adopting dimensionless input and output parameters, the model becomes inherently scalable to various tunnel sizes and configurations, eliminating the influence of scale effects and ensuring a broad range of applicability. A validated CFD model using FLACS provided the database for the ANN training, capturing the transition from deflagration to shock wave formation as well as key pressure duration and magnitude characteristics. The ANN-based model, accounting for cross-sectional area and shape, tunnel length, fuel length, blockage ratio and obstacle spacing, demonstrated acceptable predictive capability with R² values above 0.95 and most predictions within a ± 30 % error margin. Validation against large-scale experiments confirmed its reliability and practicality. This model significantly reduces computational costs and time, offering an efficient tool for predicting methane-air explosion loads in underground tunnels.

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http://hdl.handle.net/10453/191555