Mechanics-prior diffusion model for image-based data reconstruction in bridge health monitoring
Long-term bridge monitoring systems inevitably contain missing or unusable data segments after sensor faults, equipment failures, transmission interruptions, and other non-structural data-quality problems are identified and removed. Traditional reconstruction methods struggle to preserve the non-stationary, multi-scale, and sensor-dependent characteristics of bridge responses. This article proposes a mechanics-prior latent diffusion model for high-fidelity bridge monitoring data reconstruction. The method converts one-dimensional monitoring sequences into image representations, maps them into a regularized latent space through a pre-trained variational autoencoder, and trains a conditional diffusion U-Net to recover missing regions through reverse denoising. Mechanics-prior condition encoding is introduced as a global prior constraint, incorporating frequency-domain energy distribution and quasi-static morphological features tailored to different sensor types. Validation using numerical simulations and real bridge data, including acceleration, Global Positioning System (GPS) displacement, and strain, shows robust reconstruction under both long continuous gaps and fragmented missing scenarios. In representative 40% long-gap cases, the proposed method achieves NRMSE values of 0.0921, 0.0620, and 0.0523 for acceleration, GPS displacement, and strain, respectively, outperforming LSTM, GAN, and cubic spline interpolation baselines.