Structural assessment of post-impact concrete-filled steel tubular (CFST) members based on vibration tests and Bayesian model updating

Concrete-filled steel tubular (CFST) structures are widely used in construction due to their excellent mechanical properties. However, accidental impacts may cause internal damage that cannot be directly inspected. To ensure structural safety, this paper proposes a Bayesian model updating framework for rapid post-impact damage assessment of CFST members. A total of 28 circular CFST specimens were tested under different steel strengths, impact energies, boundary conditions, loading modes, and impact positions. The modal parameters were measured before and after impact and used in a Bayesian model updating procedure combined with a Timoshenko beam element model. This procedure updates the composite flexural stiffness of each element, where the highly coupled local degradation is represented by an equivalent damage parameter, β_c. Then the Bayesian method was used to effectively address parameter uncertainties and provide stable identification results. The identified stiffness degradation exhibited a clear three-zone pattern along the span, and was mainly affected by loading mode, boundary condition, and steel strength. Based on this analysis, a simplified piecewise model is developed to predict the flexural stiffness distribution. This model uses three parameters: normalized residual deflection, cross-sectional steel ratio, and a boundary condition dummy variable. Validated through rigorous leave-one-out cross-validation, this model reproduces the identified stiffness distribution with a low error. Consequently, it provides a reliable baseline tool for the rapid assessment of similar post-impact CFST members.