This paper presents an optimization methodology to simulate the monotonic and cyclic response of steel reinforcement smooth bars when subjected to inelastic buckling. A finite element (FE) model of steel rebars, based on non-linear fibre sections and an initial geometrical imperfection, is adopted. The multi-step optimization proposed herein to identify the main parameters of the material constitutive models is based on genetic algorithms (GA) and Bayesian model updating. The methodology consists of comparing available experimental tests from literature with the corresponding numerical results. New empirical relationships and probabilistic distributions of the optimized model parameters, such as post-yielding hardening ratio, isotropic hardening in compression and tension, plus initial curvature, are presented. Finally, utilizing both the GA-based and Bayesian-based calibration, an improvement of an existing analytical model for inelastic buckling of smooth steel rebars is proposed. Such analytical modelling can be efficient and reliable for future building codes and assessment guidelines for existing buildings.
Bibliographical noteFunding Information:
The authors would like to acknowledge the gracious support of this work through the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments Grant No. (EP/L015927/1). The third author is supported by the Engineering and Physical Sciences Research Council (EPSRC) project UKCRIC (EP/R012806/1).
- reinforced concrete
- genetic algorithm
- finite element modelling
- steel bars