Abstract
Limited research is available in the literature to investigate the presentation of normal strength concrete-filled steel tube (CFST) circular compression elements under compression loading by considering various material and geometric coefficients. Thus, the present study investigates the mechanical behavior of CFST compression elements employing nonlinear finite element analysis (NLFEA), empirical/theoretical modeling, and a newly developed Artificial Neural Network (ANN) model, based on a large experimental database of 1223 samples. The NLFEA modeling is performed in ABAQUS (6.14) with an improved concrete damaged plasticity model for laterally restrained concrete. Various geometric and material properties are considered, for parametric NLFEA estimates preliminary validated toward the available experimental database. A new ANN model for the load-carrying capacity of CFST circular elements was also offered by employing the experimental database. The comparison between the calculations of the NLFEA model, empirical model, and ANN model displayed a close agreement with the database results.
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