arXiv：cs.LG· Zihao Liu, Daigo Kawabe, Jiaji Wang, Chul-Woo Kim, Mehrisadat Makki Alamdari·· 4 小时前AI 评分34
基于车辆集成数字孪生与Drive-By感知的桥梁监测框架
A Vehicle-Integrated Approach to Digital Twin Deployment for Bridges Through Drive-By Sensing
AI 导读
该研究提出车辆集成数字孪生框架，用Fourier Neural Operator构建VBI与VRI代理模型，实现毫秒级推理，替代高成本全阶分析。框架还通过贝叶斯优化设计电动检测车传感器布局，并采用对抗自编码器、matrix profiles和Transformer架构的无监督损伤评估流程。完整流程已在澳大利亚和日本多站点实地试验中验证，覆盖多种桥型与交通条件。
正文
Abstract: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.
| Comments: | Extended abstract for 2nd International Conference on Engineering Structures (ICES2026) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.08822 [cs.LG] |
| (or arXiv:2610.08822v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08822 arXiv-issued DOI via DataCite |
Submission history
From: Zihao Liu [view email]
[v1]
Fri, 25 Sep 2026 01:43:59 UTC (1,047 KB)
来源：arXiv：cs.LG · arxiv.org