Soft computing approach on estimating the lateral confinement coefficient of CFRP veiled circular columns

Abstract

For any structures, Reinforced concrete (RC) columns are the utmost carping members. RC columns jacketed with CFRP membrane enhance resistance, deformation aptitude, and the auspicious confining impacts. Sundry scrutiny has been implemented about circular columns wrapping or veiled with CFRP layers. This scrutiny will manifest about Genetic Programming (GP) and Artificial Neural Networks (ANN) as soft computing models, mobbing exploited to ascertain the lateral confinement coefficient (Ks) valuate of RC circular columns jacketed with CFRP membrane. For the purpose to ascertain Ks value, manipulate the corresponding ambit such as the diameter of the column (b), column length (L), thickness layer of CFRP (tw), the elastic modulus of CFRP membrane (Ef) along with the compressive strength of unconfined concrete (fcc). Total collection of 142 datasets for the RC circular column was employed to decipher the prediction of Ks. Various statistical metrics (R2 = 0.933; RMSE = 0.054; NMBE = -0.001) compared with the ANN model and exhibits the superiority of GP in predicting the Ks. Taylor diagram and Rank analysis were also carried out in this article for justification. The lucidity of the GP machine enlightenment model overtures a greater sensible and exact prediction of Ks value for RC circular columns in accordance with versatile loading conditions.

Keywords

• ANN
• Carbon fiber reinforced polymer
• Genetic programming
• Lateral confinement coefficient
• Estimation