Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/148541 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science |
id |
RCAP_05c56e7604b2637cf6b57dc8edb1137e |
---|---|
oai_identifier_str |
oai:run.unl.pt:10362/148541 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniquesMachine LearningHigh-performance concreteCompressive strengthGradient BoostModelingDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceHigh-performance concrete (HPC) is a highly complex composite material whose characteristics are extremely difficult to model. One of those characteristics is the concrete compressive strength, a nonlinear function of the same ingredients that compose HPC: cement, fly ash, blast furnace slag, water, superplasticizer, age, and coarse and fine aggregates. Research has shown time and time again that concrete strength is not determined just by the water-to-cement ratio, which was for years the go to metric. In addition, traditional methods that attempt to model HPC, such as regression analysis, do not provide sufficient prediction power due to nonlinear proprieties of the mixture. Therefore, this study attempts to optimize the prediction and modeling of the compressive strength of HPC by analyzing seven different machine learning (ML) algorithms: three regularization algorithms (Lasso, Ridge and Elastic Net), three ensemble algorithms (Random Forest, Gradient Boost and AdaBoost), and Artificial Neural Networks. All techniques were built and tested with a dataset composed of data from 17 different concrete strength test laboratories, under the same experimental conditions, which enabled a fair comparison amongst them and between different previous studies in the field. Feature importance analysis and outlier analysis were also performed, and all models were subject to a Wilcoxon Signed-Ranks Test to ensure statistically significant results. The final results show that the more complex ML algorithms provided greater accuracy than the regularization techniques, with Gradient Boost being the superior model amongst them, providing more accurate predictions than the sate-of-the-art. Better results were achieved using all variables and without removing outlier observations.Vanneschi, LeonardoRUNMateus, Miguel Santos Reis2023-02-02T14:25:10Z2023-01-232023-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/148541TID:203211715enginfo: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:RCAAP2024-03-11T05:30:10Zoai:run.unl.pt:10362/148541Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:24.535581Repositório 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 |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques |
title |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques |
spellingShingle |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques Mateus, Miguel Santos Reis Machine Learning High-performance concrete Compressive strength Gradient Boost Modeling Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
title_short |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques |
title_full |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques |
title_fullStr |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques |
title_full_unstemmed |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques |
title_sort |
Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques |
author |
Mateus, Miguel Santos Reis |
author_facet |
Mateus, Miguel Santos Reis |
author_role |
author |
dc.contributor.none.fl_str_mv |
Vanneschi, Leonardo RUN |
dc.contributor.author.fl_str_mv |
Mateus, Miguel Santos Reis |
dc.subject.por.fl_str_mv |
Machine Learning High-performance concrete Compressive strength Gradient Boost Modeling Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
topic |
Machine Learning High-performance concrete Compressive strength Gradient Boost Modeling Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-02T14:25:10Z 2023-01-23 2023-01-23T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/148541 TID:203211715 |
url |
http://hdl.handle.net/10362/148541 |
identifier_str_mv |
TID:203211715 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
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 |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1799138124569247744 |