Spatial distribution and machine learning-based prediction model of natural gas explosion loads in a utility tunnel

Zhang, ZJ; Liu, ZX; Zhang, H; Meng, SB; Shi, JH; Zhao, JW; Wu, CQ

Spatial distribution and machine learning-based prediction model of natural gas explosion loads in a utility tunnel

Zhang, ZJ Liu, ZX Zhang, H Meng, SB Shi, JH Zhao, JW Wu, CQ

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Tunnelling and Underground Space Technology, 2023, 140
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Zhang, ZJ
dc.contributor.author	Liu, ZX
dc.contributor.author	Zhang, H
dc.contributor.author	Meng, SB
dc.contributor.author	Shi, JH
dc.contributor.author	Zhao, JW
dc.contributor.author	Wu, CQ
dc.date.accessioned	2024-04-08T08:26:52Z
dc.date.available	2024-04-08T08:26:52Z
dc.date.issued	2023-10-01
dc.identifier.citation	Tunnelling and Underground Space Technology, 2023, 140
dc.identifier.issn	0886-7798
dc.identifier.issn	1878-4364
dc.identifier.uri	http://hdl.handle.net/10453/177570
dc.description.abstract	A reasonable natural gas explosion load model is essential to evaluate the damage to the utility tunnel. This study systematically investigates critical parameters that affect gas explosion loads in the utility tunnel by the finite volume method, including ignition point location, methane volume, methane concentration, internal obstacles, and utility tunnel structure. An explosion load model in a typical utility tunnel is established using the artificial neural network (ANN), with peak overpressure, impulse, and arrival time as output parameters. The spatiotemporal distribution equation of gas explosion loads in a utility tunnel is given, and the importance analysis of parameters is also carried out. The results indicate that when the methane volume is larger than 300 m3, the detonation phenomenon generally occurs in the utility tunnel, and the effect of the obstacle on the explosion loads also increases. The ANN-based model can accurately describe the explosion overpressure and duration at each section in the utility tunnel. The methane volume and cross-sectional size of the utility tunnel have the most significant impact on the natural gas explosion. The important coefficient of the cross-sectional size of the utility tunnel for peak overpressures reaches 30.94 %, and the important coefficients of the methane volume for explosion impulses and arrival time are 47.31 % and 39.64 %. ANN-predicted model is efficient and quick to estimate the gas explosion loads for structural dynamics analysis.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation	National Natural Science Foundation of China51978186
dc.relation.ispartof	Tunnelling and Underground Space Technology
dc.relation.isbasedon	10.1016/j.tust.2023.105272
dc.rights	info:eu-repo/semantics/closedAccess
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	Spatial distribution and machine learning-based prediction model of natural gas explosion loads in a utility tunnel
dc.type	Journal Article
utslib.citation.volume	140
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/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-08T08:26:51Z
pubs.publication-status	Published
pubs.volume	140

Abstract:

A reasonable natural gas explosion load model is essential to evaluate the damage to the utility tunnel. This study systematically investigates critical parameters that affect gas explosion loads in the utility tunnel by the finite volume method, including ignition point location, methane volume, methane concentration, internal obstacles, and utility tunnel structure. An explosion load model in a typical utility tunnel is established using the artificial neural network (ANN), with peak overpressure, impulse, and arrival time as output parameters. The spatiotemporal distribution equation of gas explosion loads in a utility tunnel is given, and the importance analysis of parameters is also carried out. The results indicate that when the methane volume is larger than 300 m3, the detonation phenomenon generally occurs in the utility tunnel, and the effect of the obstacle on the explosion loads also increases. The ANN-based model can accurately describe the explosion overpressure and duration at each section in the utility tunnel. The methane volume and cross-sectional size of the utility tunnel have the most significant impact on the natural gas explosion. The important coefficient of the cross-sectional size of the utility tunnel for peak overpressures reaches 30.94 %, and the important coefficients of the methane volume for explosion impulses and arrival time are 47.31 % and 39.64 %. ANN-predicted model is efficient and quick to estimate the gas explosion loads for structural dynamics analysis.

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http://hdl.handle.net/10453/177570