Transforming Structural Health Monitoring: Leveraging Multisource Data Fusion With Two-Stage Encoder Transformer for Bridge Deformation Prediction

Bridge deformation monitoring is critical for ensuring the structural integrity and safety of bridges, as it enables early detection of potential issues that may lead to failure. However, current methods often struggle to effectively handle complex, multisource data, leading to challenges in accurately predicting deformation behaviors. To address this issue, this study proposes a two-stage encoder transformer (TSE-Transformer) that integrates the Global Navigation Satellite System (GNSS), accelerometers, and environment sensor data for accurate displacement prediction. The TSE-Transformer integrates multisource sensor data through a custom-designed encoder and a PyTorch encoder module, which together enhance its ability to model complex, nonlinear relationships and capture long-range dependencies in the data. The approach is validated through experiments and analyzed using local interpretable model-agnostic explanations (LIMEs) to determine the contribution of each sensor to the prediction model. Compared to the extreme gradient boosting (XGBoost), the proposed method demonstrates an approximate 50% improvement in accuracy. This research highlights the potential of the TSE-Transformer model to enhance the accuracy and reliability of real-time structural health monitoring, offering a robust solution for future advancements in structural health monitoring systems.