has been cited by the following article(s):
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[1]
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Machine Learning Modelling for Compressive Strength Prediction of Superplasticizer-Based Concrete
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Zadeh, A Dastmard… - Infrastructures,
2023 |
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[2]
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Artificial neural networks (ANN), MARS, and adaptive network-based fuzzy inference system (ANFIS) to predict the stress at the failure of concrete with waste steel slag …
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Neural Computing and …,
2023 |
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[3]
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Evaluation of strength, durability, and microstructure characteristics of slag-sand-induced concrete
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Cleaner Materials,
2023 |
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[4]
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Prediction of the concrete compressive strength using improved random forest algorithm
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Journal of Building …,
2023 |
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[5]
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Application of fuzzy inference system (FIS) for assessment and predication of compressive asset of concrete containing fly ash
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Materials Today …,
2022 |
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[1]
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Prediction of the concrete compressive strength using improved random forest algorithm
Journal of Building Pathology and Rehabilitation,
2023
DOI:10.1007/s41024-023-00337-8
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[2]
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Prediction of the concrete compressive strength using improved random forest algorithm
Journal of Building Pathology and Rehabilitation,
2023
DOI:10.1007/s41024-023-00337-8
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[3]
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Machine Learning Modelling for Compressive Strength Prediction of Superplasticizer-Based Concrete
Infrastructures,
2023
DOI:10.3390/infrastructures8020021
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[4]
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RETRACTED ARTICLE: Artificial neural networks (ANN), MARS, and adaptive network-based fuzzy inference system (ANFIS) to predict the stress at the failure of concrete with waste steel slag coarse aggregate replacement
Neural Computing and Applications,
2023
DOI:10.1007/s00521-023-08439-7
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[5]
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Evaluation of strength, durability, and microstructure characteristics of slag-sand-induced concrete
Cleaner Materials,
2023
DOI:10.1016/j.clema.2023.100212
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[6]
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Application of fuzzy inference system (FIS) for assessment and predication of compressive asset of concrete containing fly ash
Materials Today: Proceedings,
2022
DOI:10.1016/j.matpr.2022.08.160
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