发表机构
University of New South Wales; KyoCenseo Inc.; The University of Hong Kong; Kyoto University（新南威尔士大学; KyoCenseo公司; 香港大学; 京都大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种车辆集成数字孪生框架，结合物理建模与机器学习，通过行驶感知实现桥梁与道路连续监测，并在多国现场试验中验证其有效性。
AI 中文摘要
老化桥梁基础设施日益成为全球关注的问题，然而传统的结构健康监测（SHM）系统成本高昂且难以扩展，常规的目视检查仍具有主观性。行驶式（或称间接式）桥梁检测，即通过一辆搭载传感器的车辆从车辆-桥梁相互作用（VBI）和车辆-道路相互作用（VRI）响应中恢复结构信息，提供了一种可扩展的替代方案。然而，关键挑战仍未解决，包括从路面粗糙度中分离桥梁响应、在正常交通条件下检测损伤，以及在不同桥梁类型间进行泛化。本文提出了一种车辆集成的数字孪生框架，该框架统一了基于物理的建模和机器学习，用于桥梁与道路状况的连续监测。该框架由三个支柱组成。首先，使用傅里叶神经算子构建VBI和VRI的代理模型，该算子学习从运行条件到车辆响应的函数到函数映射。这些代理模型在模拟数据和现场数据上训练，能够提供毫秒级推理，取代计算密集型的全阶分析。其次，通过贝叶斯优化对定制电动检测车辆的设计、其传感器布局和信号处理链进行优化，以最大化桥梁信息获取量，同时抑制道路和车辆噪声。基于对抗性自编码器、矩阵轮廓和Transformer架构的无监督损伤评估流程已被开发并验证，用于处理所得车辆数据。第三，通过在澳大利亚和日本进行的协调多场地现场试验验证了完整工作流程，涵盖了多种桥梁类型、交通条件和环境设置。
英文摘要
Ageing bridge infrastructure is a growing global concern, yet conventional Structural Health Monitoring (SHM) systems are costly and difficult to scale, and routine visual inspections remain subjective. Drive-by, or indirect, bridge inspection, in which a sensorised vehicle recovers structural information from vehicle-bridge interaction (VBI) and vehicle-road interaction (VRI) responses, offers a scalable alternative. However, key challenges remain unresolved, including separating bridge responses from road roughness, detecting damage under normal traffic, and generalising across diverse bridge types. This paper presents a vehicle-integrated digital twin framework that unifies physics-based modelling and machine learning for continuous monitoring of bridge and road conditions. The framework comprises three pillars. First, surrogate models of VBI and VRI are constructed using a Fourier Neural Operator that learns function-to-function mappings from operating conditions to vehicle responses. Trained on both simulated and field data, these surrogates deliver millisecond-scale inference, replacing computationally intensive full-order analyses. Second, the design of a custom electric inspection vehicle, its sensor layout, and signal processing chain are optimised through Bayesian optimisation to maximise bridge information yield while suppressing road and vehicle noise. Unsupervised damage-assessment pipelines based on adversarial autoencoders, matrix profiles, and transformer architectures have been developed and validated to process the resulting vehicle data. Third, the complete workflow is validated through coordinated multi-site field trials in Australia and Japan, covering a range of bridge types, traffic conditions, and environmental settings.
CommentsExtended abstract for 2nd International Conference on Engineering Structures (ICES2026)
