Modeling the confining effect of carbon FRP and steel in circular RC columns using artificial neural networks
Gokongwei College of Engineering
Analytical and Computational Methods - Proceedings of the 10th East Asia-Pacific Conference on Structural Engineering and Construction, EASEC 2010
Confinement of concrete columns using steel and carbon fiber reinforced polymer (CFRP) increases the ultimate compressive strength and ductility. Since there are now extensive experimental data on confined RC columns, it may be useful to combine and reanalyze them to develop empirical models that can give reasonable predictions of the ultimate confined compressive strength of RC columns. Because of the various factors affect the compressive strength of RC columns, modeling becomes difficult especially when pre-existing transverse steel reinforcements and CFRP are both used as confining materials. This study presents the capability of artificial neural networks (ANNs) in modeling the confined compressive strength of circular RC columns. The effect of various parameters such as ρs, ρcc, ρCFRP, L, d, D, fyh, fCFRP, and f'c are considered in the development of ANN models.
Oreta, A. C., & Ongpeng, J. C. (2006). Modeling the confining effect of carbon FRP and steel in circular RC columns using artificial neural networks. Analytical and Computational Methods - Proceedings of the 10th East Asia-Pacific Conference on Structural Engineering and Construction, EASEC 2010, 2, 363-368. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/2494
Columns, Concrete--Compression testing; Fiber-reinforced concrete--Compression testing