Predicting elastic modulus degradation of alkali silica reaction affected concrete using soft computing techniques: A comparative study

Yu, Y; Nguyen, TN; Li, J; Sanchez, LFM; Nguyen, A

Predicting elastic modulus degradation of alkali silica reaction affected concrete using soft computing techniques: A comparative study

Yu, Y

Nguyen, TN Li, J

Sanchez, LFM Nguyen, A

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Construction and Building Materials, 2021, 274
Issue Date:: 2021-03-08

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Field	Value	Language
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Nguyen, TN
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048
dc.contributor.author	Sanchez, LFM
dc.contributor.author	Nguyen, A
dc.date.accessioned	2022-04-27T23:22:07Z
dc.date.available	2022-04-27T23:22:07Z
dc.date.issued	2021-03-08
dc.identifier.citation	Construction and Building Materials, 2021, 274
dc.identifier.issn	0950-0618
dc.identifier.issn	1879-0526
dc.identifier.uri	http://hdl.handle.net/10453/156710
dc.description.abstract	Alkali silica reaction (ASR) is a harmful distress mechanism which results in expansion and reduction of mechanical properties of concrete. The latter may cause loss of serviceability and load carrying capacity of affected concrete structures. Influences of ASR on concrete are known to be complex in nature, for which the traditional empirical and curve-fitting approaches are insufficient to provide adequate models to capture such complexity. Recent advancement in soft computing (SC) offers a new tool for tackling the complexity of ASR affected concrete. Most of previous experimental studies agreed that as a result of ASR, the elastic modulus suffers a significant reduction compared with other properties such as compressive and tensile strength of the affected concrete. In this study, an investigation has been conducted, utilising different SC models to quantify ASR-induced elastic modulus degradation of unrestrained concrete. Five SC techniques, namely support vector machine (SVM), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), M5P model and genetic expression programming (GEP), are investigated comparatively in this research. The models, on basis of SC techniques, are developed and tested using a comprehensive dataset collected from existing publications. In order to demonstrate the superiorities of SC techniques, the proposed approaches are compared to several empirical models developed using same dataset. The comparative results show that the developed SC models outperform empirical models in a wide range of evaluation indices, which indicates promising applications of the proposed approach.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation	http://purl.org/au-research/grants/arc/IH150100006
dc.relation	Ausgrid
dc.relation.ispartof	Construction and Building Materials
dc.relation.isbasedon	10.1016/j.conbuildmat.2020.122024
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0905 Civil Engineering, 1202 Building
dc.subject.classification	Building & Construction
dc.title	Predicting elastic modulus degradation of alkali silica reaction affected concrete using soft computing techniques: A comparative study
dc.type	Journal Article
utslib.citation.volume	274
utslib.for	0905 Civil Engineering
utslib.for	1202 Building
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	open_access	*
utslib.copyright.embargo	2023-03-08T00:00:00+1000Z
dc.date.updated	2022-04-27T23:22:05Z
pubs.publication-status	Published
pubs.volume	274

Abstract:

Alkali silica reaction (ASR) is a harmful distress mechanism which results in expansion and reduction of mechanical properties of concrete. The latter may cause loss of serviceability and load carrying capacity of affected concrete structures. Influences of ASR on concrete are known to be complex in nature, for which the traditional empirical and curve-fitting approaches are insufficient to provide adequate models to capture such complexity. Recent advancement in soft computing (SC) offers a new tool for tackling the complexity of ASR affected concrete. Most of previous experimental studies agreed that as a result of ASR, the elastic modulus suffers a significant reduction compared with other properties such as compressive and tensile strength of the affected concrete. In this study, an investigation has been conducted, utilising different SC models to quantify ASR-induced elastic modulus degradation of unrestrained concrete. Five SC techniques, namely support vector machine (SVM), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), M5P model and genetic expression programming (GEP), are investigated comparatively in this research. The models, on basis of SC techniques, are developed and tested using a comprehensive dataset collected from existing publications. In order to demonstrate the superiorities of SC techniques, the proposed approaches are compared to several empirical models developed using same dataset. The comparative results show that the developed SC models outperform empirical models in a wide range of evaluation indices, which indicates promising applications of the proposed approach.

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http://hdl.handle.net/10453/156710