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Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms

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dc.citedby19
dc.contributor.authorAbdellatief M.en_US
dc.contributor.authorWong L.S.en_US
dc.contributor.authorDin N.M.en_US
dc.contributor.authorMo K.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57855303900en_US
dc.contributor.authorid55504782500en_US
dc.contributor.authorid9335429400en_US
dc.contributor.authorid55915884700en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:42:22Z
dc.date.available2025-03-03T07:42:22Z
dc.date.issued2024
dc.description.abstractArtificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimental trials to attain specific properties such as compressive strength (CS). In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. During the training phase, 75 % of the experimental dataset was utilized. The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. In comparison, the GPR algorithm reveals high-accuracy towards the estimation of CS, as proved by its high R2-value, which equals 0.98, while the R2 for ANN, SVR, and LR are 0.97, 0.90, and 0.89, respectively. Additionally, parametric and sensitivity analyses were used to assess the performance of the GPR and LR algorithms. Results revealed that density exerted the most significant influence on CS, with the GPR model showing a pronounced negative impact of fine aggregate-to-cement ratio on CS, particularly in low-density FC, contrasting with the LR model. This study confirmed that the GPR algorithm provided reliable accuracy in predicting the CS of FC. Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo110022
dc.identifier.doi10.1016/j.mtcomm.2024.110022
dc.identifier.scopus2-s2.0-85200420453
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85200420453&doi=10.1016%2fj.mtcomm.2024.110022&partnerID=40&md5=26d447b78dba66730a6a0118cdf92048
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36426
dc.identifier.volume40
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleMaterials Today Communications
dc.subjectCements
dc.subjectConcrete aggregates
dc.subjectConcrete mixing
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectMachine learning
dc.subjectMean square error
dc.subjectMultilayer neural networks
dc.subjectParameter estimation
dc.subjectRegression analysis
dc.subjectSensitivity analysis
dc.subjectArtificial intelligence algorithms
dc.subjectCompressive strength prediction- parametric analyse
dc.subjectFoam concretes
dc.subjectGaussian process regression
dc.subjectInput variables
dc.subjectMachine learning algorithms
dc.subjectParametric analysis
dc.subjectRegression algorithms
dc.subjectStrength prediction
dc.subjectSupport vector regressions
dc.subjectCompressive strength
dc.titleEvaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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