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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms

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dc.citedby15
dc.contributor.authorZiyad Sami B.H.en_US
dc.contributor.authorZiyad Sami B.F.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAmieghemen G.E.en_US
dc.contributor.authorSherif M.M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57481263600en_US
dc.contributor.authorid57481286200en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid58089424300en_US
dc.contributor.authorid57188776821en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:18:19Z
dc.date.available2024-10-14T03:18:19Z
dc.date.issued2023
dc.description.abstractConcrete is the most utilized material (i.e., average production of 2 billion tons per year) for the construction of buildings, bridges, roads, dams, and several other important infrastructures. The strength and durability of these structures largely depend on the compressive strength of the concrete. The compressive strength of concrete depends on the proportionality of the key constituents (i.e., fine aggregate, coarse aggregate, cement, and water). However, the optimization of the constituent proportions (i.e., matrix design) to achieve high-strength concrete is a challenging task. Furthermore, it is essential to reduce the carbon footprint of the cementitious composites through the optimization of the matrix. In this research, machine learning algorithms including regression models, tree regression models, support vector regression (SVR), ensemble regression (ER), and gaussian process regression (GPR) were utilized to predict the compressive and tensile concrete strength. Also, the model performance was characterized based on the number of input variables utilized. The dataset used in this research was compiled from journal publications. The results showed that the exponential GPR had the highest performance and accuracy. The model had an impressive performance during the training phase, with a R2 of 0.98, RMSE of 2.412 MPa, and MAE of 1.6249 MPa when using 8 input variables to predict the compressive strength of concrete. In the testing phase, the model maintained its accuracy with a R2 of 0.99, RMSE of 0.0025134 MPa, and MAE of 0.0016367 MPa. In the training and testing phases, the exponential GPR also demonstrated high accuracy in predicting the tensile strength with an R2, RMSE, and MAE of 0.99, 0.00049247 MPa, and 0.00036929 MPa, respectively. Furthermore, in the prediction of tensile strength the number of variables utilized had an insignificant effect on the performance of the models. However, in predicting the compressive strength, an increase in the number of input variables lead to an enhancement in the performance metrics. The results of this research can allow for the quick and accurate prediction of the strength of a given concrete mixture design. � 2023en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe01893
dc.identifier.doi10.1016/j.cscm.2023.e01893
dc.identifier.scopus2-s2.0-85147332991
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85147332991&doi=10.1016%2fj.cscm.2023.e01893&partnerID=40&md5=d5407469ce8ad7b2762dc5b632fc69ff
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34183
dc.identifier.volume18
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleCase Studies in Construction Materials
dc.subjectCompressive strength
dc.subjectConcrete
dc.subjectMachine learning
dc.subjectPredictive modeling
dc.subjectTensile strength
dc.subjectBridges
dc.subjectCarbon footprint
dc.subjectConcrete aggregates
dc.subjectConcrete mixtures
dc.subjectForecasting
dc.subjectHigh performance concrete
dc.subjectLearning algorithms
dc.subjectMachine learning
dc.subjectMatrix algebra
dc.subjectRegression analysis
dc.subjectStructural design
dc.subjectTensile strength
dc.subjectTensile testing
dc.subjectCompressive strength of concrete
dc.subjectConcrete
dc.subjectGaussian process regression
dc.subjectInput variables
dc.subjectMachine learning algorithms
dc.subjectMachine-learning
dc.subjectOptimisations
dc.subjectPerformance
dc.subjectPredictive models
dc.subjectRegression modelling
dc.subjectCompressive strength
dc.titleFeasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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