PSI - Issue 84

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 84 (2026) 959–966

© 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Conference Keywords: Bayesian model updating; damage assessment; model-based monitoring; bridges; surrogate models. Abstract Efficient and reliable damage detection in bridges requires models that capture complex structural behaviours while remaining computationally tractable for real-time application. Conventional finite element (FE) model updating is often infeasible due to its high computational cost and the large parameter space involved. This paper proposes a multi-surrogate framework for bridge Structural Health Monitoring (SHM) that balances model fidelity with computational efficiency. A global surrogate is first trained to emulate the bridge’s dynamic response across a broad set of potential damage parameters. From this, multiple local surrogate models are derived, each varying only a small subset of parameters corresponding to plausible damage locations, such as specific spans, girders, or supports. These surrogates provide computationally efficient approximations of structural dynamics under competing possible damage conditions. In operation, vibration sensor data are assimilated into the surrogates via Bayesian inference, and statistical metrics such as the Bayesian Information Criterion (BIC) are employed to rank scenarios while maintaining low computational cost. To ensure a realistic validation, the method is tested with real monitoring data from an instrumented bridge, where the surrogate model is trained on one FE model and damage scenarios are simulated with a separate, independently calibrated model. III Fabre Conference: Existing Bridges, Viaducts, and Tunnels: Research, Innovation, and Applications A Bayesian-based multi-surrogate framework for bridge Structural Health Monitoring Laura Ierimonti a, *, Elisa Tomassini a , Enrique García-Macías b , Filippo Ubertini a a Department of civil and Environmental Engineering, University of Perugia, Via G. Duranti, 06125, Italy b Department of Structural Mechanics and Hydraulic Engineering, University of Granada, Av. Fuentenueva sn, 18002 Granada, Spain.

* Corresponding author. E-mail address: laura.ierimonti@unipg.it

2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Conference 10.1016/j.prostr.2026.06.123