Predicting the ultimate axial capacity of uniaxially loaded cfst columns using multiphysics artificial intelligence

• Main Authors: Khan, S., Khan, M.A., Zafar, A., Javed, M.F., Aslam, F., Musarat, M.A., Vatin, N.I.
• Format: Article
• Published: MDPI 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121715454&doi=10.3390%2fma15010039&partnerID=40&md5=fa579baf45c28583aa70785da293bd1b; http://eprints.utp.edu.my/28911/

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The data...