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.

由于缺乏实验数据、建模复杂性或计算成本高昂，有限元 (FE) 建模通常需要不可避免的简化或假设。其中一种简化是对腐蚀现象或材料特性进行建模，通常假设它们在整个结构中是均匀的。然而，例如，腐蚀具有局部性质，并对钢结构的性能产生严重后果，这是不容忽视的。为了改进当前老化钢桥的数值建模技术，本文提出了一种基于高斯 Copula 的贝叶斯网络（GCBN）方法来模拟结构元素属性的空间变异性。因此，首先对贝叶斯网络自动生成过程进行研究。随后，该方法被应用于一座建于 1897 年的严重损坏的铆接钢桥。结果表明，该方法具有出色的灵活性，可以以较低的计算成本生成有限元模型中的属性变异性，从而确保其精确数值建模的实际可行性和鲁棒性。