MODEL PREDICTION FOR COMPRESSIVE STRENGTH OF A FULLY CONFINED CONCRETE CYLINDER WITH CARBON FIBRE REINFORCED POLYMER

• Published in: JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY
• Main Authors: Shahrin, Wan Muhammad; Ismail, Ruqayyah; Lee, Hoong Pin; Goh, Lyn Dee; Ahmad, Fariz Aswan; Ahmad, Hazrina; Wahid, Norlizan
• Format: Article
• Language: English
• Published: TAYLORS UNIV SDN BHD 2023
• Subjects: Engineering
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001154412900017
• ISSN: 1823-4690

Nowadays, the application of carbon fibre reinforced polymer (CFRP) composites in engineering works for strengthening of reinforced concrete structures is increase dramatically. CFRP can be used to strengthen the structural elements to increase its performance in load carrying capacity, and subsequently delaying the deterioration rate or reducing the impact of damage, if any. This paper aims to provide an analytical model which is capable to predict the CFRP fully confined concrete compressive strength. This analytical model is developed by using artificial neural network (ANN) which utilised the data from a new database created from the previous experimental works in previous literatures. Four input parameters are selected as the training parameters for the ANN, i.e. the tensile strength of CFRP (f(f)), thickness of the CFRP layer (t), CFRP's Young modulus of elasticity (E-f) and compressive strength of unconfined concrete (f(co)). The output of the ANN models is to predict the compressive strength of confined concrete (f(cc)). In addition, a comparison was carried out with the predicted value from the proposed ANN model in this study and the experimental value from literature, and with two other existing mathematical models from previous study. The proposed ANN model showed lowest average error in predicting the experimental results with only a difference of 5.91 MPa as compared to the actual experimental value.