Indirect structural health monitoring (iSHM) offers a scalable and cost-effective alternative to dense, permanently installed sensor networks by leveraging instrumented vehicles to collect bridge response data during normal operation, [1]. However, reliable damage localisation and quantification from vehicle-measured responses remain fundamentally challenging due to the ill-posed nature of the inverse problem, variability in vehicle–bridge interaction, and strong contamination of vehicle signals by road roughness. In this work, we consider a vehicle–bridge interaction framework where the bridge is modelled as an Euler–Bernoulli beam with spatially varying stiffness governed by two unknown parameters representing damage location and severity. Two quarter-car vehicle models - with and without damping - are investigated. Vehicle acceleration is treated as the only source of measurement data. The inverse damage localisation and quantification problem is addressed by integrating bridge modal decomposition, a semi-analytical solution of the vehicle vibration equation, and a kriging-assisted optimisation strategy. Unknown mode shapes and natural frequencies of the damaged bridge are reconstructed using physics-informed neural networks, [2], by minimising the Rayleigh quotient of the beam eigenvalue problem. The networks are pre-trained on healthy bridge modes, enabling efficient learning of small damage-induced deviations. Numerical results demonstrate that the proposed framework achieves accurate damage identification using a single bridge mode and a single vehicle passage over the bridge under road class A roughness conditions. References: [1] Y.B. Yang, C.W. Lin, J.D. Yau, Extracting bridge frequencies from the dynamic response of a passing vehicle, J. Sound Vib., 272 (3) (2004), pp. 471-493 [2] M. Raissi, P. Perdikaris, G.E. Karniadakis, Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys. 378 (2019) 686–707.