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This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 958171. This work has been partially supported by the Spanish Ministry of Science and Innovation through the PONT3 project Ref. PID2021-124236OB-C33. This work has also been supported by the Spanish Ministry of Science, Innovation, and Universities through the grant PRE2019-087331 for the training of predoctoral researchers. This document reflects only the views of the authors. Neither the Innovation and Networks Executive Agency (INEA) nor the European Commission is in any way responsible for any use that may be made of the information it contains.

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Gaussian Copula-based Bayesian network approach for characterizing spatial variability in aging steel bridges

Publicado en:Structural Safety. 106 102403- - 2023-11-06 106(), DOI: 10.1016/j.strusafe.2023.102403

Autores: Barros, B; Conde, B; Riveiro, B; Morales-Napoles, O

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Finite Element (FE) modeling often requires unavoidable simplifications or assumptions due to a lack of experimental data, modeling complexity, or non-affordable computational cost. One such simplification is modeling corrosion phenomena or material properties, which are usually assumed to be uniform throughout the structure. However, e.g., corrosion has a local nature and severe consequences on the behavior of steel structures that should not be overlooked. To improve the current numerical modeling techniques in aging steel bridges, this paper proposes a Gaussian Copula-based Bayesian Network (GCBN) approach to model the spatial variability of structural element properties. Accordingly, a study of the automatic Bayesian network generation process is first conducted. Subsequently, the methodology is applied to a severely damaged riveted steel bridge built in 1897. The results show that the methodology has excellent flexibility for generating properties variability in FE models at a low computational cost, thus ensuring its practical feasibility and robustness for accurate numerical modeling.

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