ANFIS modelling of the strength properties of natural rubber latex modified concrete

The increasing demand for sustainable construction materials has driven research into innovative modifications to enhance concrete performance while reducing environmental impact. This study investigates the optimization of Natural Rubber Latex Modified Concrete (NRLMC) using an Adaptive Neuro-Fuzzy...

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Veröffentlicht in:	Discover applied sciences Jg. 7; H. 5; S. 472 - 37
Hauptverfasser:	Nyah, Efiok Etim, Onwuka, David Ogbonna, Arimanwa, Joan Ijeoma, Alaneme, George Uwadiegwu, Nakkeeran, G., Onwuka, Ulari Sylvia, Okere, Chinenye Elizabeth
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 09.05.2025 Springer Nature B.V Springer
Schlagworte:	Accuracy Adaptive systems Algorithms Aluminum Applied and Technical Physics Artificial intelligence Calcium sulfate Cement Chemistry/Food Science Compressive strength Concrete mixing Concrete structures Construction Destabilization Durability Earth Sciences Engineering Environment Environmental impact Fuzzy logic Green infrastructure Latex Machine learning Materials Science Mechanical properties Microstructural analysis Natural rubber Natural rubber latex Neural networks Neuro-fuzzy models Optimization Porosity Regression analysis Renewable resources Research methodology Resource utilization Rubber Scanning electron microscopy Splitting Sustainability Sustainable concrete Sustainable development Sustainable materials Tensile properties Tensile strength X-ray spectroscopy Artificial intelligence Neuro-fuzzy models Natural rubber latex Sustainable concrete
ISSN:	3004-9261, 2523-3963, 3004-9261, 2523-3971
Online-Zugang:	Volltext
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