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dc.contributorEscuela Superior y Tecnica de Ingenieros de Minases_ES
dc.contributor.authorMartínez García, Rebeca 
dc.contributor.authorPalencia Coto, María Covadonga 
dc.contributor.authorJagadesh, P
dc.contributor.authorDe Prado Gil, Jesús
dc.contributor.otherIngenieria Mecanicaes_ES
dc.date2022
dc.date.accessioned2024-05-10T10:52:07Z
dc.date.available2024-05-10T10:52:07Z
dc.identifier.citationde-Prado-Gil, J.; Palencia, C.; Jagadesh, P.; Martínez-García, R. A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete. Materials 2022, 15, 4164. https://doi.org/10.3390/ma15124164es_ES
dc.identifier.urihttps://hdl.handle.net/10612/20584
dc.description.abstract[EN] Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.es_ES
dc.languageenges_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectIngeniería de minases_ES
dc.subject.otherMachine Learninges_ES
dc.subject.otherSplitting Tensile Strengthes_ES
dc.subject.otherSelf-compacting Concretees_ES
dc.subject.otherRecycled Aggregateses_ES
dc.subject.otherPredictiones_ES
dc.titleA Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concretees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.3390/MA15124164
dc.description.peerreviewedSIes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.essn1996-1944
dc.journal.titleMaterialses_ES
dc.volume.number15es_ES
dc.issue.number12es_ES
dc.page.initial4164es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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