Bridge Girder-End Displacement Reconstruction Using a Novel Hybrid Attention Mechanism Leveraging Multisource Information

Abstract

In the realm of structural health monitoring (SHM) of bridge structures, the accurate reconstruction of girder-end displacement (GED) is crucial for identifying potential structural damage and ensuring the monitoring system’s reliability. A novel fine-grained spatial (FGS) attention mechanism, combined with efficient channel attention (ECA), has been proposed to effectively utilize multisource monitoring data. This hybrid attention mechanism has been integrated into an arithmetic optimization algorithm–bidirectional long short-term memory (AOA–BiLSTM) framework for reconstructing GED using non-GED data, including deflection, temperature, strain, and traffic data. Data are organized into a two-dimensional array based on sensor types and spatial locations to capture interchannel and intrachannel correlations. ECA captures local correlations among different sensor types, while the proposed FGS enhances model interpretability by focusing on local dependencies within each sensor type. Huber loss is employed for robust performance, and AOA techniques are used for efficient hyperparameter optimization. Validation with real-world data from a cable-stayed bridge demonstrates the necessity and efficacy of considering multidimensional information correlations in response reconstruction for SHM applications. This work lays a theoretical foundation for improving safety assessments, anomaly detection, data recovery, and virtual sensing in bridge structures.