AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface

Katlav, Metin; Ergen, Faruk; Donmez, Izzeddin

doi:10.1016/j.mtcomm.2024.109915

Materials Today Communications

2024

DOI: 10.1016/j.mtcomm.2024.109915

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AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface

Metin Katlav,

Faruk Ergen,

Izzeddin Donmez

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AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface

Cited by 3 publications

References 69 publications

Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies

Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies

A robust approach for bond strength prediction of mortar using machine learning with SHAP interpretability

An experimental investigation to predict the compressive strength of lightweight Ceramsite aggregate UHPC using boosting and bagging techniques

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