Explainable ensemble learning predictive model for thermal conductivity of cement-based foam

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dc.authoridCakiroglu, Celal/0000-0001-7329-1230
dc.contributor.authorCakiroglu, Celal
dc.contributor.authorBatool, Farnaz
dc.contributor.authorIslam, Kamrul
dc.contributor.authorNehdi, Moncef L.
dc.date.accessioned2025-02-20T08:42:19Z
dc.date.available2025-02-20T08:42:19Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.description.abstractCement-based foam has emerged as a strong contender in sustainable construction owing to its superior thermal and sound insulation properties, fire resistance, and cost-effectiveness. To effectively use cement-based foam as a thermal insulation material, it is important to accurately predict its thermal conductivity. The current study aims at coining an accurate methodology for predicting the thermal conductivity of cement-based foam using state-ofthe-art machine learning techniques. A comprehensive experimental dataset of 504 data points was developed and used for training ensemble learning models including XGBoost, CatBoost, LightGBM and Random Forest. The independent variables of this dataset affecting the thermal conductivity are the cast density, percentage of pozzolan, porosity, percentage of moisture, and duration of hydration in days. Using the Isolation Forest algorithm proved effective in detecting and eliminating outliers in the dataset. All the ensemble learning techniques explored in this study achieved superior predictive accuracy with a coefficient of determination greater than 0.98 on the test dataset. The influence of the input features on the thermal conductivity was visualized using the SHapley Additive exPlanations (SHAP) approach and individual conditional expectation (ICE) plots. The cast density had the greatest effect on thermal conductivity. The explainable machine learning models demonstrated superior accuracy, efficiency, and reliability in estimating the thermal insulation of cement-based foam, opening the door for wider acceptance of this material in sustainable energy efficient construction.
dc.identifier.doi10.1016/j.conbuildmat.2024.135663
dc.identifier.issn0950-0618
dc.identifier.issn1879-0526
dc.identifier.scopus2-s2.0-85186685439
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.conbuildmat.2024.135663
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1646
dc.identifier.volume421en_US
dc.identifier.wosWOS:001208735200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofConstruction and Building Materials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250220
dc.subjectCement-based foamen_US
dc.subjectThermal conductivityen_US
dc.subjectModel predictionen_US
dc.subjectMachine learningen_US
dc.subjectExplainableen_US
dc.subjectEnsemble Learningen_US
dc.titleExplainable ensemble learning predictive model for thermal conductivity of cement-based foam
dc.typeArticle

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