Bayesian model selection for the shear hysteretic behavior of recycled rubber-fiber reinforced elastomeric isolators

Recycled rubber fiber-reinforced elastomeric isolators (RR-FREIs) represent a sustainable and cost-effective alternative for seismic protection, particularly in low-rise structures in developing countries. Despite their practical advantages, accurately modeling their complex hysteretic shear behavior remains challenging due to nonlinearities and parameter uncertainties. Accordingly, this study aims to determine, within a probabilistic framework, which hysteretic formulation provides the most robust and physically consistent representation of the cyclic shear response of RR-FREIs. Four OpenSeesPy hysteretic models are investigated: Elastomeric Bearing (Plasticity), Elastomeric Bearing (Bouc–Wen), ElastomericX, and HDR. Bayesian inference, implemented through Markov Chain Monte Carlo sampling, is employed to calibrate the models against experimental cyclic shear tests of scaled prototypes, allowing for explicit quantification of parameter uncertainty. Model performance is evaluated through posterior predictive checks, energy dissipation metrics, and Bayesian information criteria (WAIC and LOO). Unlike conventional deterministic calibration approaches, the proposed methodology enables a probabilistic and statistically consistent comparison between competing hysteretic models while explicitly quantifying parameter uncertainty and its influence on the predicted cyclic response. Results indicate that the Bouc–Wen formulation provides the most robust agreement with experimental data, accurately reproducing both hysteresis loops and energy dissipation trends. The proposed framework offers a rational and physically interpretable basis for hysteretic model selection in performance-based seismic analysis and supports the reliable implementation of RR-FREIs in earthquake-resistant design.