Performance of different machine learning techniques in predicting the flexural capacity of concrete beams reinforced with FRP rods-Reference-Cited by-同舟云学术

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Performance of different machine learning techniques in predicting the flexural capacity of concrete beams reinforced with FRP rods

Published:2023-07-15 Issue:1 Volume:25 Page:525-536
ISSN:1563-0854
Container-title:Asian Journal of Civil Engineering
language:en
Short-container-title:Asian J Civ Eng

Author:

Ngamkhanong Chayut,Alzabeebee Saif,Keawsawasvong Suraparb,Thongchom Chanachai

Publisher

Springer Science and Business Media LLC

Subject

Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42107-023-00792-1.pdf

Reference51 articles.

1. Abed, F., & Alhafiz, A. R. (2019). Effect of basalt fibers on the flexural behavior of concrete beams reinforced with BFRP bars. Composite Structures, 215, 23–34. https://doi.org/10.1016/j.compstruct.2019.02.050

2. Abuodeh, O. R., Abdalla, J. A., & Hawileh, R. A. (2020). Assessment of compressive strength of ultra-high performance concrete using deep machine learning techniques. Applied Soft Computing., 95, 106552. https://doi.org/10.1016/j.asoc.2020.106552

3. Ahmed, M. M., Abdel Hafez, A. M., Assaf, K. A., & Mohamed, A. K. (2014). Flexural behavior of flexural behavior of basalt FRP RC beams under repeated load. Journal of Engineering Sciences., 42(5), 1179–1192. https://doi.org/10.21608/JESAUN.2014.115060

4. Al-Hamd, R. K. S., Alzabeebee, S., Cunningham, L. S., & Gales, J. (2022). Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach. Fire and Materials. https://doi.org/10.1002/fam.3123

5. Almustafa, M. K., & Nehdi, M. L. (2020). Machine learning model for predicting structural response of RC slabs exposed to blast loading. Engineering Structures., 221, 111109. https://doi.org/10.1016/j.engstruct.2020.111109

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