The Application of a BiGRU Model with Transformer-Based Error Correction in Deformation Prediction for Bridge SHM

Buildings, Journal Year: 2025, Volume and Issue: 15(4), P. 542 - 542

Published: Feb. 10, 2025

Accurate deformation prediction is crucial for ensuring the safety and longevity of bridges. However, complex fluctuations pose a challenge to achieving this goal. To improve accuracy, bridge method based on bidirectional gated recurrent unit (BiGRU) neural network error correction proposed. Firstly, BiGRU model employed predict data, which aims enhance modeling capability GRU time-series data through its structure. Then, extract valuable information concealed in error, transformer introduced rectify sequence. Finally, preliminary results are integrated yield high-precision results. Two datasets collected from an actual health monitoring system utilized as examples verify effectiveness proposed method. The show that outperforms comparison terms robustness, generalization ability, with predicted being closer Notably, error-corrected exhibits significantly improved evaluation metrics compared single model. research findings herein offer scientific foundation bridges’ early warning monitoring. Additionally, they hold significant relevance developing models deep learning.

Language: Английский

PMformer: A Novel Informer-based Model for Accurate Long-Term Time Series Prediction

Information Sciences, Journal Year: 2024, Volume and Issue: unknown, P. 121586 - 121586

Published: Oct. 1, 2024

Language: Английский

Real-time prediction model of passenger thermal comfort for intelligent cabin

International Journal of Thermal Sciences, Journal Year: 2024, Volume and Issue: 207, P. 109370 - 109370

Published: Aug. 27, 2024

Language: Английский