TY - JOUR
T1 - Gaussian Copula-based Bayesian network approach for characterizing spatial variability in aging steel bridges
AU - Barros, B.
AU - Conde, B.
AU - Riveiro, B.
AU - Morales-Nápoles, O.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Aging steel bridges
KW - FE modeling
KW - Gaussian copula-based Bayesian network
KW - Random field
UR - http://www.scopus.com/inward/record.url?scp=85176432497&partnerID=8YFLogxK
U2 - 10.1016/j.strusafe.2023.102403
DO - 10.1016/j.strusafe.2023.102403
M3 - Article
AN - SCOPUS:85176432497
SN - 0167-4730
VL - 106
JO - Structural Safety
JF - Structural Safety
M1 - 102403
ER -