Research graph
References from Machine Learning Framework with Posthoc Explainability for Predicting the Strength of Mechanochemically Activated Geopolymer Paste. Local targets link to admitted publications; unresolved targets remain external evidence.
Low-carbon cementitious materials: Scale-up potential, environmental impact and barriers
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Unresolved reference
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The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
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An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements
2021 · External reference
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Unresolved reference
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2024 · External reference
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Unresolved reference
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Unresolved reference
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Unresolved reference
External reference
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10.1016/0022-1694(70)90255-6 · External reference
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10.1016/j.ijrefrig.2024.01.025 · External reference
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2024 · External reference
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10.1007/s43615-021-00029-w · External reference
10.1016/j.conbuildmat.2015.08.009
10.1016/j.conbuildmat.2015.08.009 · External reference
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10.3390/app12199729 · External reference
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10.1016/j.jclepro.2020.123697 · External reference
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2021 · External reference
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10.1139/cjce-2020-0558 · External reference
Durability assessment of mechanochemically activated geopolymer concrete with a low molarity alkali solution
2024 · External reference
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10.1016/j.conbuildmat.2019.117455 · External reference
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10.1016/j.jmatprotec.2009.03.016 · External reference
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10.3390/ma18194456 · External reference
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2025 · External reference
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