NUMERICAL INVESTIGATION OF HYBRID-FIBER-REINFORCED CONCRETE COLUMNS USING THE CONCRETE DAMAGED PLASTICITY MODEL IN ABAQUS
Keywords:
hybrid-fiber-reinforced concrete; steel fiber; polypropylene fiber; reinforced concrete columns; ABAQUS; finite element analysisAbstract
This study presents a nonlinear finite element investigation of short rectangular reinforced concrete columns incorporating hybrid steel and polypropylene fibers using the Concrete Damaged Plasticity (CDP) model implemented in ABAQUS Standard. The numerical investigation was conducted on 21 concentrically loaded column models using previously reported experimental results as the reference dataset. The modeled columns had a rectangular cross-section of 150 × 150 mm and a height of 1200 mm. The numerical framework incorporated concrete, longitudinal and transverse reinforcement, steel loading plates, contact constraints, material properties, boundary conditions, mesh discretization, and axial loading. Model calibration was performed by examining the sensitivity of the response to CDP parameters, including dilation angle, viscosity parameter, flow-potential eccentricity, and the shape factor KcK_c. Following calibration, the numerical predictions were compared with experimental peak loads, axial deformation at peak load, load–deformation behavior, and crack patterns. The calibrated control specimen M-6 used a dilation angle of 42°, viscosity parameter of 0.0075, and mesh size of 40 mm. Across the 21 specimens, the ABAQUS models reproduced the experimental peak-load response with generally good agreement, although considerably larger differences occurred for axial deformation in several specimens. The results indicate that CDP-based nonlinear finite element analysis can provide a useful computational framework for examining the behavior of hybrid-fiber-reinforced concrete columns and for conducting parametric investigations that would otherwise require extensive experimental testing. The study also demonstrates that careful calibration of CDP parameters is essential for obtaining stable and representative numerical predictions.












