Incorporation of radial basis function with Gorilla Troops Optimization and Moth-Flame Optimization to predict the compressive strength of high-performance concrete
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Mechanics of Materials,General Materials Science
Link
https://link.springer.com/content/pdf/10.1007/s41939-023-00169-6.pdf
Reference45 articles.
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2. Abellán García J, Fernández Gómez J, Torres Castellanos N (2020) Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks. Eur J Environ Civ Eng 26(6):2319–2343
3. Akbari M, JafariDeligani V (2020) Data driven models for compressive strength prediction of concrete at high temperatures. Front Struct Civ Eng 14(2):311–321
4. Ali M, Kotb H, Aboras KM, Abbasy NH (2021) Design of cascaded PI-fractional order PID controller for improving the frequency response of hybrid microgrid system using gorilla troops optimizer. IEEE Access 9:150715–150732
5. AlOmar MK, Hameed MM, Al-Ansari N, AlSaadi MA (2020) Data-driven model for the prediction of total dissolved gas: robust artificial intelligence approach. Adv Civ Eng 1(1):1–20
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1. XGB-Northern Goshawk Optimization: Predicting the Compressive Strength of Self-Compacting Concrete;KSCE Journal of Civil Engineering;2024-01-18
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