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    Effectiveness of wood waste sawdust to produce medium- to low-strength concrete materials
    (Elsevier, 2021) Batool, Farnaz; Islam, Kamrul; Çakıroğlu, Celal; Shahriar, Anjuman
    This paper investigates the use of sawdust as fine aggregate and its influence on the properties of hardened concrete, and examines the correlation between sawdust content and hydration days. In this study, untreated wood sawdust from a wood factory in Bangladesh is added to concrete mixtures. Concrete mixtures prepared by replacing fine aggregates with sawdust in the ratio of 10%-60% are evaluated for compressive, tensile, and flexural strength along with sulphate resistance for four different hydration periods. In addition, the microstructure of sawdust concrete is studied using scanning electron microscopic images. The micrographs show a wider formation of cracks, openings, and interface gaps in the cement matrix with the addition of sawdust. However, after sulphate immersion, the gaps and cracks are found to contract due to the ettringite filler effect. The addition of sawdust is found to reduce the workability and to have an adverse effect with increasing replacement levels. Similarly, reduced density of the hardened concrete is observed in the case of the sawdust concrete mixtures. Experimental results and cost comparison reveal that ten percent sawdust substitution is found to be optimal and low-cost replacement to natural fine aggregates with respect to the hardened properties, as it yields better performance in comparison with the other replacement ratios. Also, sulphate immersion for a period of 28 days is found to improve the compressive strength, even for mixtures with higher sawdust content. Moreover, a model is developed to predict the compressive and tensile strength of sawdust concrete using regression analysis.
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    Explainable ensemble learning predictive model for thermal conductivity of cement-based foam
    (Elsevier Sci Ltd, 2024) Cakiroglu, Celal; Batool, Farnaz; Islam, Kamrul; Nehdi, Moncef L.
    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-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.