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

Celal Cakiroglu, Farnaz Batool, Kamrul Islam and Moncef L. Nehdi

Article (2024)

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Cement-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-of-the-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.

Uncontrolled Keywords

cement-based foam; thermal conductivity; model prediction; machine learning; explainable; ensemble learning

Subjects: 1000 Civil engineering > 1000 Civil engineering
1000 Civil engineering > 1001 Construction engineering and management
Department: Department of Civil, Geological and Mining Engineering
PolyPublie URL: https://publications.polymtl.ca/57846/
Journal Title: Construction and Building Materials (vol. 421)
Publisher: Elsevier
DOI: 10.1016/j.conbuildmat.2024.135663
Official URL: https://doi.org/10.1016/j.conbuildmat.2024.135663
Date Deposited: 28 Mar 2024 15:20
Last Modified: 01 May 2024 23:34
Cite in APA 7: Cakiroglu, C., Batool, F., Islam, K., & Nehdi, M. L. (2024). Explainable ensemble learning predictive model for thermal conductivity of cement-based foam. Construction and Building Materials, 421, 135663 (15 pages). https://doi.org/10.1016/j.conbuildmat.2024.135663


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