Physics-Constrained Identification and OpenSees Deployment of a Twelve-Parameter BWBN Model for RC Column Hysteresis

Accurate simulation of reinforced concrete (RC) members under cyclic loading requires hysteresis models that capture degradation and pinching, yet inverse identification of such models remains challenging because the internal-state evolution is strongly coupled and sensitive to incremental consistency. This study develops a physics-constrained, model-based framework to identify the full twelve-parameter Bouc–Wen–Baber–Noori (BWBN) model directly from cyclic force–displacement records and to deploy the calibrated parameters in OpenSees. Parameter estimation is posed as a bound-constrained nonlinear least-squares problem, where each objective evaluation advances the BWBN internal variables through a discrete incremental constitutive update and accumulates the energy-driven deterioration measure using a consistent trapezoidal work integration. Validation on nine RC column tests covering flexural, flexural–shear, and shear failures shows good agreement between simulated and experimental hysteresis loops, with R2 ranging from 0.956 to 0.986 and RMSE ranging from 0.06 to 0.09 over the full records. Unlike simpler hysteresis models that omit degradation and pinching, the calibrated BWBN model reproduces mode-dependent deterioration and reloading pinching, and the identified parameters can be used directly in OpenSees for subsequent nonlinear simulations.