September 10, 2025BuildingsOpen Access

Longitudinal Displacement Reconstruction Method of Suspension Bridge End Considering Multi-Type Data Under Deep Learning Framework

View Full Paper

Ask AI

Bookmark

Authors

Discussion

Analysis reconstructs missing longitudinal displacement data in suspension bridges using LSTM, indicating improved prediction accuracy.

Prediction accuracy of longitudinal displacement improves significantly when using both temperature and rotation angle data.
Long short-term memory models yield better predictions of longitudinal displacement than other input data methods.
Temperature data inside the box girder provides optimal results for predicting longitudinal displacement in suspension bridges.
The use of multi-type data highlights the importance of comprehensive health monitoring in bridge infrastructure.

Yang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark