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

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 stre...

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Autores principales:	Hamed Izadgoshasb, Amirreza Kandiri, Pshtiwan Shakor, Vittoria Laghi, Giada Gasparini
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-objective optimization artificial neural network compressive strength 3DP mortar additive manufacturing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/230e30dcbcbb4d04aaab068db5d640a4
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id	oai:doaj.org-article:230e30dcbcbb4d04aaab068db5d640a4
record_format	dspace
spelling	oai:doaj.org-article:230e30dcbcbb4d04aaab068db5d640a42021-11-25T16:38:51ZPredicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning10.3390/app1122108262076-3417https://doaj.org/article/230e30dcbcbb4d04aaab068db5d640a42021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10826https://doaj.org/toc/2076-3417Machine 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.Hamed IzadgoshasbAmirreza KandiriPshtiwan ShakorVittoria LaghiGiada GaspariniMDPI AGarticlemulti-objective optimizationartificial neural networkcompressive strength3DP mortaradditive manufacturingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10826, p 10826 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-objective optimization artificial neural network compressive strength 3DP mortar additive manufacturing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	multi-objective optimization artificial neural network compressive strength 3DP mortar additive manufacturing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Hamed Izadgoshasb Amirreza Kandiri Pshtiwan Shakor Vittoria Laghi Giada Gasparini Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
description	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.
format	article
author	Hamed Izadgoshasb Amirreza Kandiri Pshtiwan Shakor Vittoria Laghi Giada Gasparini
author_facet	Hamed Izadgoshasb Amirreza Kandiri Pshtiwan Shakor Vittoria Laghi Giada Gasparini
author_sort	Hamed Izadgoshasb
title	Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
title_short	Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
title_full	Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
title_fullStr	Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
title_full_unstemmed	Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
title_sort	predicting compressive strength of 3d printed mortar in structural members using machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/230e30dcbcbb4d04aaab068db5d640a4
work_keys_str_mv	AT hamedizadgoshasb predictingcompressivestrengthof3dprintedmortarinstructuralmembersusingmachinelearning AT amirrezakandiri predictingcompressivestrengthof3dprintedmortarinstructuralmembersusingmachinelearning AT pshtiwanshakor predictingcompressivestrengthof3dprintedmortarinstructuralmembersusingmachinelearning AT vittorialaghi predictingcompressivestrengthof3dprintedmortarinstructuralmembersusingmachinelearning AT giadagasparini predictingcompressivestrengthof3dprintedmortarinstructuralmembersusingmachinelearning
_version_	1718413065855172608

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

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