Publication:
Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study

Simple item page
dc.citedby22
dc.contributor.authorAbdellatief M.en_US
dc.contributor.authorHassan Y.M.en_US
dc.contributor.authorElnabwy M.T.en_US
dc.contributor.authorWong L.S.en_US
dc.contributor.authorChin R.J.en_US
dc.contributor.authorMo K.H.en_US
dc.contributor.authorid57855303900en_US
dc.contributor.authorid57216270212en_US
dc.contributor.authorid55848885000en_US
dc.contributor.authorid55504782500en_US
dc.contributor.authorid57189222458en_US
dc.contributor.authorid55915884700en_US
dc.date.accessioned2025-03-03T07:42:12Z
dc.date.available2025-03-03T07:42:12Z
dc.date.issued2024
dc.description.abstractUltra-high-performance geopolymer concrete (UHPGC) is a new category of traditional UHPC developed to meet the desire for ultra-high-strength and green building materials. In the current study, random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB) are used to forecast the compressive strength (CS) of UHPGC. Firstly, the findings of the 113 CS tests available in the previous studies were extracted. Twelve feature variables, including GGBS, silica fume, fly ash, and rice husk ash contents as precursors, the Na2SiO3, NaOH, KOH, and extra water content, polypropylene fiber, steel fiber, liquid-to-binder (L/B) ratio, and curing temperature, were investigated. After analyzing the extracted data, it was found that there were more mixtures of steel fiber-based UHPGCs and synthetic fibers compared to mixtures without fibers. This may reduce the accuracy and comprehensiveness of the predictive models used. To address this issue, several experiments were designed, performed, and tested. Overall, the dataset of 128 CS results was used to develop the machine learning (ML) models. The findings validate the effectiveness of the RF, SVR, and XGB models in accurately predicting the strength of the UHPGC, as constructed by their excellent predictive accuracy (R2 > 0.84). The XGB model performance is superior to the RF and SVR models. The feature importance analysis determined that the steel fiber content and L/B ratio were the top two elements that might profoundly impact the CS. Additionally, NaOH and silica fume also have a positive correlation with CS. Conversely, the extra water and percentage of GGBS exhibit a low correlation with the CS. Through the application of ML models, this study not only ascertains the significance of algorithms including RF, SVR, and XGB in precisely forecasting the CS of UHPGC, but also reveals essential understandings regarding the importance of steel fiber content, L/B ratio, and various other pivotal variables, consequently facilitating the development of refined formulations and improved functionalities in eco-friendly construction materials. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo136884
dc.identifier.doi10.1016/j.conbuildmat.2024.136884
dc.identifier.scopus2-s2.0-85195300809
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85195300809&doi=10.1016%2fj.conbuildmat.2024.136884&partnerID=40&md5=8018b5283639b82c799454ed03cb7175
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36392
dc.identifier.volume436
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleConstruction and Building Materials
dc.subjectAdaptive boosting
dc.subjectFly ash
dc.subjectForecasting
dc.subjectForestry
dc.subjectGeopolymers
dc.subjectHigh performance concrete
dc.subjectInorganic polymers
dc.subjectPolypropylenes
dc.subjectPotassium hydroxide
dc.subjectSilica fume
dc.subjectSodium hydroxide
dc.subjectSteel fibers
dc.subjectBinder ratio
dc.subjectCompressive strength prediction- extreme gradient boosting
dc.subjectGeopolymer concrete
dc.subjectGradient boosting
dc.subjectMachine learning models
dc.subjectRandom forests
dc.subjectStrength prediction
dc.subjectSupport vector regressions
dc.subjectUltra high performance
dc.subjectUltra-high-performance geopolymer concrete
dc.subjectCompressive strength
dc.titleInvestigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative studyen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS