Infusing deep learning with structural engineering has received widespread attention for both forward problems (structural simulation) and inverse problems (structural health monitoring). Based on Fourier Neural Operator, this study proposes VINO (Vehicle-bridge Interaction Neural Operator) to serve as the digital twin of bridge structures. VINO learns mappings between structural response fields and damage fields. In this study, VBI-FE dataset was established by running parametric finite element (FE) simulations considering a random distribution of structural initial damage field. Subsequently, VBI-EXP dataset was produced by conducting an experimental study under four damage scenarios. After VINO was pre-trained by VBI-FE and fine-tuned by VBI-EXP from the bridge at the healthy state, the model achieved the following two improvements. First, forward VINO can predict structural responses from damage field inputs more accurately than the FE model. Second, inverse VINO can determine, localize, and quantify damages in all scenarios, suggesting the practicality of data-driven approaches.

翻译：将深度学习与结构工程相结合，在正问题（结构仿真）和逆问题（结构健康监测）中均受到广泛关注。本研究基于傅里叶神经算子，提出车辆-桥梁交互神经算子VINO（Vehicle-bridge Interaction Neural Operator）作为桥梁结构的数字孪生。VINO学习结构响应场与损伤场之间的映射关系。本研究通过参数化有限元仿真（考虑结构初始损伤场的随机分布）建立了VBI-FE数据集。随后，在四种损伤工况下开展实验研究，建立了VBI-EXP数据集。VINO经VBI-FE预训练及桥梁健康状态下的VBI-EXP微调后，实现以下两项改进：首先，正向VINO可根据损伤场输入预测结构响应，其精度优于有限元模型；其次，逆向VINO能识别、定位并量化所有工况下的损伤，表明数据驱动方法的实用性。