Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles

The use of nano-materials to improve the engineering properties of different types of concrete composites including geopolymer concrete (GPC) has recently gained popularity. Numerous programs have been executed to investigate the mechanical properties of GPC. In general, compressive strength (CS) is...

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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 17; S. 12453 - 12479
Hauptverfasser:	Ahmed, Hemn Unis, Mohammed, Ahmed S., Faraj, Rabar H., Abdalla, Aso A., Qaidi, Shaker M. A., Sor, Nadhim Hamah, Mohammed, Azad A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.06.2023 Springer Nature B.V
Schlagworte:	Artificial Intelligence Artificial neural networks Cement Civil engineering Compressive strength Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Concrete mixing Curing Data Mining and Knowledge Discovery Engineering schools Geopolymers Image Processing and Computer Vision Machine learning Mechanical properties Modelling Nanoparticles Neural networks Original Article Polymerization Probability and Statistics in Computer Science Sensitivity analysis Statistical analysis Variables Compressive strength Sensitivity analysis Artificial neural network Multi-expression programming Modeling Geopolymer concrete
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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