A Vehicle-Integrated Approach to Digital Twin Deployment for Bridges Through Drive-By Sensing

A new arXiv paper (arXiv:2610.08822v1) presents a vehicle-integrated digital twin framework that unifies physics-based modelling and machine learning for continuous monitoring of bridge and road conditions through drive-by sensing. The framework uses Fourier Neural Operator surrogate models of vehicle-bridge and vehicle-road interaction that deliver millisecond-scale inference, a custom electric inspection vehicle whose sensor layout and signal processing chain are tuned via Bayesian optimisation, and unsupervised damage-assessment pipelines built on adversarial autoencoders, matrix profiles, and transformer architectures. The complete workflow was validated through coordinated multi-site field trials in Australia and Japan across a range of bridge types, traffic conditions, and environmental settings.

arXiv:2610.08822v1 Announce Type: new 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.