Assessing external sulfate attack on thin-shell artificial reef structures under uncertainty

Yu, Y; Gao, W; Castel, A; Liu, A; Chen, X; Liu, M

Assessing external sulfate attack on thin-shell artificial reef structures under uncertainty

Yu, Y Gao, W Castel, A

Liu, A Chen, X Liu, M

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Publication Type:: Journal Article
Citation:: Ocean Engineering, 2020, 207
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Yu, Y
dc.contributor.author	Gao, W
dc.contributor.author	Castel, A https://orcid.org/0000-0003-1619-9643
dc.contributor.author	Liu, A
dc.contributor.author	Chen, X
dc.contributor.author	Liu, M
dc.date.accessioned	2020-05-20T03:18:52Z
dc.date.available	2020-05-20T03:18:52Z
dc.date.issued	2020-07-01
dc.identifier.citation	Ocean Engineering, 2020, 207
dc.identifier.issn	0029-8018
dc.identifier.uri	http://hdl.handle.net/10453/140836
dc.description.abstract	© 2020 Elsevier Ltd Thin-shell artificial reef (AR) structures with spatial internal volumes have demonstrated superior stock recruitment ability and material efficiency than many gravity-based reef blocks, and cementitious materials, given the easy-to-tailor nature, remains the most popular in reef constructions to date. However, under constant seawater immersion, external sulfate attack (ESA) introduces a major and uncertain reliability concern to this type of AR system, due to the inherent material randomness. This study is concerned with developing a novel stochastic modelling framework for assessing the ESA under material uncertainty. In this paper, the main difficulty associated with the stochastic ESA modelling is identified for the first time, and a novel machine learning aided chemophysical modelling approach is proposed. The performance of the developed framework is carefully examined through the analyses on two types of cementitious materials under ESA.
dc.language	en
dc.relation	http://purl.org/au-research/grants/arc/IH150100006
dc.relation	http://purl.org/au-research/grants/arc/DP160104731
dc.relation.ispartof	Ocean Engineering
dc.relation.isbasedon	10.1016/j.oceaneng.2020.107397
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0405 Oceanography, 0905 Civil Engineering, 0911 Maritime Engineering
dc.subject.classification	Civil Engineering
dc.title	Assessing external sulfate attack on thin-shell artificial reef structures under uncertainty
dc.type	Journal Article
utslib.citation.volume	207
utslib.for	0405 Oceanography
utslib.for	0905 Civil Engineering
utslib.for	0911 Maritime Engineering
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
utslib.copyright.status	closed_access	*
dc.date.updated	2020-05-20T03:18:47Z
pubs.publication-status	Accepted
pubs.volume	207

Abstract:

© 2020 Elsevier Ltd Thin-shell artificial reef (AR) structures with spatial internal volumes have demonstrated superior stock recruitment ability and material efficiency than many gravity-based reef blocks, and cementitious materials, given the easy-to-tailor nature, remains the most popular in reef constructions to date. However, under constant seawater immersion, external sulfate attack (ESA) introduces a major and uncertain reliability concern to this type of AR system, due to the inherent material randomness. This study is concerned with developing a novel stochastic modelling framework for assessing the ESA under material uncertainty. In this paper, the main difficulty associated with the stochastic ESA modelling is identified for the first time, and a novel machine learning aided chemophysical modelling approach is proposed. The performance of the developed framework is carefully examined through the analyses on two types of cementitious materials under ESA.

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http://hdl.handle.net/10453/140836