Journal
ENGINEERING STRUCTURES
Volume 240, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2021.112378
Keywords
Modelling; Reinforced Concrete; Genetic algorithm; Finite element modelling; Buckling; steel bars
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Funding
- EPSRC
- ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments Grant [EP/L015927/1]
- Engineering and Physical Sciences Research Council (EPSRC) project UKCRIC [EP/R012806/1]
- EPSRC [EP/R012806/1] Funding Source: UKRI
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This paper presents an optimization methodology for simulating the monotonic and cyclic response of steel reinforcement smooth bars subjected to inelastic buckling, by comparing experimental tests with numerical results to optimize material constitutive model parameters and proposing new empirical relationships and probabilistic distributions. Additionally, an improvement of an existing analytical model for inelastic buckling of smooth steel rebars is proposed through GA-based and Bayesian-based calibration methods.
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.
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