Modeling the confining effect of carbon FRP and steel in circular RC columns using artificial neural networks

• Main Authors: Oreta, Andres Winston C., Ongpeng, Jason Maximino C.
• Format: text
• Published: Animo Repository 2006
• Subjects: Columns, Concrete > Compression testing; Fiber-reinforced concrete > Compression testing; Civil Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2494
• Institution: De La Salle University

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.