PSI - Issue 84

Laura Ierimonti et al. / Procedia Structural Integrity 84 (2026) 959–966

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Fig. 3. Bic values for the analyzed SM.

The posterior distributions of θ₉ ,.., θ₁₂ are illustrated in Figure 4 and are consistent with the simulated damage scenario while incorporating prior information. The results show that the inferred mean values slightly exceed the imposed damage level, which is attributed to the influence of the prior distributions and the uncertainty introduced by model discrepancies. Notably, SM 3 yields well-concentrated posterior distributions with relatively narrow credible intervals, indicating robust parameter identifiability and stable inference. These findings confirm that the Bayesian model class selection decisively favors reduced-order surrogate models, with SM 3 providing the optimal trade-off between model fidelity, interpretability, and statistical robustness. The inferior performance of SM 6 highlights the importance of aligning model complexity with data informativeness.

Fig. 4. Posterior distributions with the indication of mean values and standard deviation for SM 3 .

6. Conclusions This paper proposed a multi-surrogate Bayesian framework for efficient bridge damage detection that balances model fidelity and computational efficiency for SHM applications. By combining a global surrogate with multiple low-dimensional local surrogates, the approach enables rapid evaluation of competing damage scenarios.