AI 中文总结
提出利用检测车辆振动数据构建桥梁数字孪生的框架，结合FNO、车辆优化和虚拟传感，实现快速仿真与损伤评估。
AI 中文摘要
项目“桥梁数字孪生部署的车辆集成方法”正在开发一个可扩展的框架，利用来自配备传感器的检测车辆的振动测量来进行桥梁状态评估。该方法结合了车辆-桥梁相互作用建模、状态估计、机器学习和替代建模，以减少对永久传感器网络的依赖。日本的老阿达桥是主要案例研究，因为它提供了在多种实验引入的损伤状态下的直接和基于车辆的现场测量。进展已在三个方向取得。首先，前向和逆向傅里叶神经算子（FNO）模型已在基准梁上得到验证，能够实现快速响应预测以及损伤位置和严重程度的识别。其次，开发了一个检测车辆优化框架，用于选择车辆质量和轮胎悬架刚度，以最大化健康与损伤响应之间的分离，该框架整合了接触点重建、损伤评估、克里金法和粒子群优化。第三，开发了一个基于增广卡尔曼滤波器的虚拟传感框架，用于估计移动荷载，并从稀疏传感器重建未测量位置的位移、速度和加速度。下一阶段将使用老阿达桥模型和现场数据扩展并整合这些方法。FNO模型将迁移到桁架结构，并使用完好和损伤的测量数据进行评估。车辆优化将测试其可迁移性，而虚拟传感将扩展到耦合的车辆-桥梁系统，以估计路面粗糙度、车辆参数、移动力和结构响应。最终，这些组件将形成一个集成的车辆驱动数字孪生，用于在现实条件下进行快速仿真、响应重建和损伤评估。
英文摘要
The project A Vehicle Integrated Approach to Digital Twin Deployment for Bridges is developing a scalable framework for bridge condition assessment using vibration measurements from a sensorised inspection vehicle. It combines vehicle bridge interaction modelling, state estimation, machine learning and surrogate modelling to reduce reliance on permanent sensor networks. The Old Ada Bridge in Japan is the principal case study because it provides direct and vehicle-based field measurements across multiple experimentally introduced damage states. Progress has been made across three streams. First, forward and inverse Fourier Neural Operator (FNO) models have been demonstrated on a benchmark beam, enabling rapid response prediction and identification of damage location and severity. Second, an inspection vehicle optimisation framework has been developed to select vehicle mass and tyre suspension stiffness that maximise separation between healthy and damaged responses, integrating contact-point reconstruction, damage assessment, Kriging and particle swarm optimisation. Third, an Augmented Kalman Filter-based virtual sensing framework has been developed to estimate moving loads and reconstruct displacement, velocity and acceleration at unmeasured locations from sparse sensors. The next phase will extend and integrate these methods using the Old Ada Bridge model and field data. FNO models will be transferred to the truss structure and evaluated using intact and damaged measurements. Vehicle optimisation will be tested for transferability, while virtual sensing will be extended to the coupled vehicle bridge system to estimate road roughness, vehicle parameters, moving forces and structural responses. Ultimately, these components will form an integrated vehicle-driven digital twin for rapid simulation, response reconstruction and damage assessment under realistic conditions.
