Bridge deformation quantiles prediction with MVO-CNN-BiLSTM based on mixed attention mechanism and periodic multi-source information fusion

An accurate understanding of anticipated deflection behavior in operational bridges allows for the early identification of potential structural anomalies. While structural monitoring systems collect extensive data, much of it remains underutilized for precise bridge health assessments. This paper proposes a deep learning method that leverages multi-source information fusion to enhance the analysis of bridge monitoring data. Firstly, the mutual information method is introduced for optimal selection of multi-source data, and then cyclic encoding is included in the multi-source data to further enhance the model's capability of learning periodic features. Then, the deep learning model Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) is employed in this study considering a mixed attention mechanism combining Squeeze-and-Excitation (SE) attention and soft attention, which is proposed to extract high-value information from the multi-source data, enabling the model to fully utilize the multi-source information and improve prediction accuracy. The Multi-Verse Optimizer method is employed to optimize the model parameters, and a quantile loss function is defined for objective interval prediction. Finally, the algorithm is validated using three sets of in-situ monitoring data from a cable-stayed bridge, and the performance of the algorithm is evaluated based on both point prediction and interval (quantile) prediction accuracies. The results show that the proposed method can make full use of the fused multi-source information, and has an evident advantage in prediction accuracy compared to other algorithms. Moreover, the predicted deflection intervals have the highest effective density, providing a theoretical basis for real-time warning for in-service bridges.