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

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

Ultra-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 forec...

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Bibliographic Details
Main Authors:	Abdellatief M., Hassan Y.M., Elnabwy M.T., Wong L.S., Chin R.J., Mo K.H.
Other Authors:	57855303900
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Adaptive boosting Fly ash Forecasting Forestry Geopolymers High performance concrete Inorganic polymers Polypropylenes Potassium hydroxide Silica fume Sodium hydroxide Steel fibers Binder ratio Compressive strength prediction- extreme gradient boosting Geopolymer concrete Gradient boosting Machine learning models Random forests Strength prediction Support vector regressions Ultra high performance Ultra-high-performance geopolymer concrete Compressive strength
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Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study

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