Enhanced data imputation framework for bridge health monitoring using Wasserstein generative adversarial networks with gradient penalty

Abstract

The availability of complete data is essential for accurately assessing structural stability and condition in structural health monitoring (SHM) systems. Unfortunately, data missing is a common occurrence in daily monitoring operations, which hinders real-time analysis and evaluation of structural conditions. Although considerable research has been conducted to efficiently recover missing data, the implementation of these recovery methods often encounters issues such as serious mode collapse and gradient vanishing. To address these challenges, this paper proposes a missing data imputation framework called WGAIN-GP based on Wasserstein Generative Adversarial Network with Gradient Penalty. This framework aims to enhance the stability and convergence rate of the network during the missing data recovery process. The effectiveness and robustness of the proposed method are extensively evaluated using measured acceleration data from a long-span highway-railway dual-purpose bridge. The results of the implementation demonstrate that the proposed method achieves superior recovery performance even under various missing data conditions, including high missing rates of up to 90%. Furthermore, the generality of the method is validated by successfully recovering data from different missing sensors. Additionally, the recovered data is utilized for modal analysis of the bridge's structural state, further verifying the reliability of the recovery method. The proposed recovery method offers several advantages, with its stability and robustness being particularly noteworthy. By significantly enhancing the reliability of the recovered data, this method contributes to improving the overall accuracy and effectiveness of structural health monitoring systems.