PSI - Issue 78

Pooria Mesbahi et al. / Procedia Structural Integrity 78 (2026) 1839–1846

1846

Within this framework, Bayesian Networks o ff er a powerful tool to represent probabilistic dependencies, while Bayesian model updating supplies new evidence for the transfer learning process. Their combination enables intuitive reasoning about uncertainty propagation and conditional relationships between parameters and observed data. Future research could focus on extending the methodology to continuous BNs instead of discrete ones, and on developing algorithms to generalize the probabilistic FEMU procedure. Such improvements would facilitate hyperpa rameter selection, increase adaptability, and ensure more stable convergence.

Acknowledgements

Support by FABRE – “Research consortium for the evaluation and monitoring of bridges, viaducts and other structures” (www.consorziofabre.it / en) within the activities of the FABRE-ANAS 2021-2025 research program is gratefully acknowledged by the authors. Any opinions expressed in the paper do not necessarily reflect the views of the funder.

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