Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System

Detalhes bibliográficos
Autor(a) principal: Vanneschi, Leonardo
Data de Publicação: 2018
Outros Autores: Castelli, Mauro, Scott, Kristen, Popovič, Aleš
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.1186/s40069-018-0300-5
Resumo: Vanneschi, L., Castelli, M., Scott, K., & Popovič, A. (2018). Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System. International Journal of Concrete Structures and Materials, 12(1), [72]. DOI: 10.1186/s40069-018-0300-5
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spelling Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming Systemartificial intelligencegenetic programminghigh performance concretesemantic awarenessstrength predictionCivil and Structural EngineeringOcean EngineeringSDG 9 - Industry, Innovation, and InfrastructureSDG 11 - Sustainable Cities and CommunitiesVanneschi, L., Castelli, M., Scott, K., & Popovič, A. (2018). Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System. International Journal of Concrete Structures and Materials, 12(1), [72]. DOI: 10.1186/s40069-018-0300-5In 2013, our research group published a contribution in which a new version of genetic programming, called Geometric Semantic Genetic Programming (GSGP), was fostered as an appropriate computational intelligence method for predicting the strength of high-performance concrete. That successful work, in which GSGP was shown to outperform the existing systems, allowed us to promote GSGP as the new state-of-the-art technology for high-performance concrete strength prediction. In this paper, we propose, for the first time, a novel genetic programming system called Nested Align Genetic Programming (NAGP). NAGP exploits semantic awareness in a completely different way compared to GSGP. The reported experimental results show that NAGP is able to significantly outperform GSGP for high-performance concrete strength prediction. More specifically, not only NAGP is able to obtain more accurate predictions than GSGP, but NAGP is also able to generate predictive models with a much smaller size, and thus easier to understand and interpret, than the ones generated by GSGP. Thanks to this ability of NAGP, we are able here to show the model evolved by NAGP, which was impossible for GSGP.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNVanneschi, LeonardoCastelli, MauroScott, KristenPopovič, Aleš2018-12-06T23:12:54Z2018-12-012018-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttps://doi.org/10.1186/s40069-018-0300-5eng1976-0485PURE: 6545736http://www.scopus.com/inward/record.url?scp=85057105555&partnerID=8YFLogxKhttps://doi.org/10.1186/s40069-018-0300-5info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:26:34Zoai:run.unl.pt:10362/53891Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:32:42.148847Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
title Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
spellingShingle Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
Vanneschi, Leonardo
artificial intelligence
genetic programming
high performance concrete
semantic awareness
strength prediction
Civil and Structural Engineering
Ocean Engineering
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
title_short Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
title_full Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
title_fullStr Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
title_full_unstemmed Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
title_sort Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System
author Vanneschi, Leonardo
author_facet Vanneschi, Leonardo
Castelli, Mauro
Scott, Kristen
Popovič, Aleš
author_role author
author2 Castelli, Mauro
Scott, Kristen
Popovič, Aleš
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Vanneschi, Leonardo
Castelli, Mauro
Scott, Kristen
Popovič, Aleš
dc.subject.por.fl_str_mv artificial intelligence
genetic programming
high performance concrete
semantic awareness
strength prediction
Civil and Structural Engineering
Ocean Engineering
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
topic artificial intelligence
genetic programming
high performance concrete
semantic awareness
strength prediction
Civil and Structural Engineering
Ocean Engineering
SDG 9 - Industry, Innovation, and Infrastructure
SDG 11 - Sustainable Cities and Communities
description Vanneschi, L., Castelli, M., Scott, K., & Popovič, A. (2018). Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System. International Journal of Concrete Structures and Materials, 12(1), [72]. DOI: 10.1186/s40069-018-0300-5
publishDate 2018
dc.date.none.fl_str_mv 2018-12-06T23:12:54Z
2018-12-01
2018-12-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.1186/s40069-018-0300-5
url https://doi.org/10.1186/s40069-018-0300-5
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1976-0485
PURE: 6545736
http://www.scopus.com/inward/record.url?scp=85057105555&partnerID=8YFLogxK
https://doi.org/10.1186/s40069-018-0300-5
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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