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Compressive strength of nano concrete materials under elevated temperatures using machine learning.

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第一作者： Abdullah M,Zeyad

第一单位： Civil and Architectural Engineering Department, College of Engineering and Computer Sciences, Jazan University, Jazan 45142, Saudi Arabia., Jazan University, Jazan, Kingdom of Saudi Arabia. azmohsen@jazanu.edu.sa.

作者： Abdullah M,Zeyad ^[1] ; Alaa A,Mahmoud ^[2] ; Alaa A,El-Sayed ^[2] ; Ayman M,Aboraya ^[3] ; Islam N,Fathy ^[4] ; Nikos,Zygouris ^[5] ; Panagiotis G,Asteris ^[5] ; Ibrahim Saad,Agwa ^[6]

作者单位： Civil and Architectural Engineering Department, College of Engineering and Computer Sciences, Jazan University, Jazan 45142, Saudi Arabia., Jazan University, Jazan, Kingdom of Saudi Arabia. azmohsen@jazanu.edu.sa. ^[1] Civil Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt. ^[2] Construction and Building Engineering Department, Higher Institute of Engineering, Culture & Science City, Giza, Egypt. ^[3] Civil Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt.;Construction and Building Engineering Department, October High Institute for Engineering & Technology, Giza, Egypt. ^[4] Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, 15122, Greece. ^[5] Department of Civil and Architectural Constructions, Faculty of Technology and Education, Suez University, P.O.Box: 43221, Suez, Egypt. ^[6]

关键词 Artificial neural networks Elevated temperature Fuzzy logic models Genetic algorithms Machine learning Nano additives Water Cycle Algorithm

DOI 10.1038/s41598-024-73713-0

PMID 39414873

发布时间 2024-10-19

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