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Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

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dc.citedby14
dc.contributor.authorAbdullah G.M.S.en_US
dc.contributor.authorAhmad M.en_US
dc.contributor.authorBabur M.en_US
dc.contributor.authorBadshah M.U.en_US
dc.contributor.authorAl-Mansob R.A.en_US
dc.contributor.authorGamil Y.en_US
dc.contributor.authorFawad M.en_US
dc.contributor.authorid56606096100en_US
dc.contributor.authorid58731610900en_US
dc.contributor.authorid57191503633en_US
dc.contributor.authorid58849655200en_US
dc.contributor.authorid55566434500en_US
dc.contributor.authorid57191379149en_US
dc.contributor.authorid57949408000en_US
dc.date.accessioned2025-03-03T07:41:32Z
dc.date.available2025-03-03T07:41:32Z
dc.date.issued2024
dc.description.abstractThe present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS. ? 2024, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2323
dc.identifier.doi10.1038/s41598-024-52825-7
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85183357076
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85183357076&doi=10.1038%2fs41598-024-52825-7&partnerID=40&md5=277b14fec6355723871a1a57fea43eed
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36190
dc.identifier.volume14
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.subjectalkali
dc.subjectsodium hydroxide
dc.subjectarticle
dc.subjectartificial neural network
dc.subjectcompressive strength
dc.subjectdata base
dc.subjectfly ash
dc.subjectfurnace
dc.subjecthuman
dc.subjectmachine learning
dc.subjectmean absolute error
dc.subjectmean squared error
dc.subjectrandom forest
dc.subjectreliability
dc.subjectroot mean squared error
dc.subjectsensitivity analysis
dc.subjectslag
dc.subjectsoil
dc.titleBoosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soilen_US
dc.typeArticleen_US
dspace.entity.typePublication
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