Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

Izadgoshasb, H; Kandiri, A; Shakor, P; Laghi, V; Gasparini, G

Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

Izadgoshasb, H Kandiri, A Shakor, P

Laghi, V Gasparini, G

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Applied Sciences, 2021, 11, (22), pp. 10826-10826
Issue Date:: 2021-11-16

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Field	Value	Language
dc.contributor.author	Izadgoshasb, H
dc.contributor.author	Kandiri, A
dc.contributor.author	Shakor, P https://orcid.org/0000-0002-6617-261X
dc.contributor.author	Laghi, V
dc.contributor.author	Gasparini, G
dc.date.accessioned	2021-12-05T05:57:57Z
dc.date.available	2021-12-05T05:57:57Z
dc.date.issued	2021-11-16
dc.identifier.citation	Applied Sciences, 2021, 11, (22), pp. 10826-10826
dc.identifier.issn	1454-5101
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/152090
dc.description.abstract	<jats:p>Machine learning is the discipline of learning commands in the computer machine to predict and expect the results of real application and is currently the most promising simulation in artificial intelligence. This paper aims at using different algorithms to calculate and predict the compressive strength of extrusion 3DP concrete (cement mortar). The investigation is carried out using multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN). Given that the accuracy of a machine learning method depends on the number of data records, and for concrete 3D printing, this number is limited to few years of study, this work develops a new method by combining both methodologies into an ANNMOGOA approach to predict the compressive strength of 3D-printed concrete. Some promising results in the iteration process are achieved.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Applied Sciences
dc.relation.isbasedon	10.3390/app112210826
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
dc.type	Journal Article
utslib.citation.volume	11
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.date.updated	2021-12-05T05:57:45Z
pubs.issue	22
pubs.publication-status	Published online
pubs.volume	11
utslib.citation.issue	22

Abstract:

Machine learning is the discipline of learning commands in the computer machine to predict and expect the results of real application and is currently the most promising simulation in artificial intelligence. This paper aims at using different algorithms to calculate and predict the compressive strength of extrusion 3DP concrete (cement mortar). The investigation is carried out using multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN). Given that the accuracy of a machine learning method depends on the number of data records, and for concrete 3D printing, this number is limited to few years of study, this work develops a new method by combining both methodologies into an ANNMOGOA approach to predict the compressive strength of 3D-printed concrete. Some promising results in the iteration process are achieved.

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http://hdl.handle.net/10453/152090