Modeling the confined compressive strength of hybrid circular concrete columns using neural networks

• Main Authors: Oreta, Andres Winston C., Ongpeng, Jason M.C.
• Format: text
• Published: Animo Repository 2011
• Subjects: Carbon fibers—Compression testing; Columns, Concrete—Compression testing; Buildings—Retrofitting; Neural networks (Computer science); Civil Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2495; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3494/type/native/viewcontent
• Institution: De La Salle University

With respect to rehabilitation, strengthening and retrofitting of existing and deteriorated columns in buildings and bridges, CFRP sheets have been found effective in enhancing the performance of existing RC columns by wrapping and bonding CFRP sheets externally around the concrete. Concrete columns and piers that are confined by both lateral steel reinforcement and CFRP are sometimes referred to as "hybrid" concrete columns. With the availability of experimental data on concrete columns confined by steel reinforcement and/or CFRP, the study presents modeling using artificial neural networks (ANNs) to predict the compressive strength of hybrid circular RC columns. The prediction of the ultimate confined compressive strength of RC columns is very important especially when this value is used in estimating the capacity of structures. The present ANN model used as parameters for the confining materials the lateral steel ratio (ρs) and the FRP volumetric ratio (ρFRP). The model gave good predictions for three types of confined columns: (a) columns confined with steel reinforcement only, (b) CFRP confined columns, and (c) hybrid columns confined by both steel and CFRP. The model may be used for predicting the compressive strength of existing circular RC columns confined with steel only that will be strengthened or retrofitted using CFRP.