Deep learning-driven forecasting of bridge thermal-induced structural response with feature engineering

Abstract

In order to forecast temperature-induced strains and deflections of bridges, this study introduces a deep learning framework enhanced with feature engineering. Utilizing 1 year of field-measured temperature (input), strain and deflection data (output) from the Queensferry Crossing cable-stayed bridge, an LSTM-Attention model is developed as the core predictive architecture, with its performance evaluated both with and without feature engineering, which comprises lag feature, difference feature, rolling standard deviation and moving average. Meanwhile, comparative analyses against LSTM-Attention model and other proposed models are conducted to assess the contribution of feature engineering and attention mechanisms. Results reveal that feature engineering significantly improves predictive accuracy, particularly when integrated with the LSTM-Attention framework. For both strain and deflection forecasting, the feature-engineered LSTM-Attention model achieves up to an 84.32% reduction in $MAE$ and a 98.39% decrease in $MSE$ , alongside notable improvements in R² and EVS (more than 28.57% and 25.32% respectively). Additionally, the attention mechanism also presents considerable function in improving the predictive accuracy by decreasing more than 38.98% in $MAE$ and 60.38% in $MSE$ for strains and deflections, increasing over 3.13% in $R^2$ and 2.06% in $EVS$ for strains, while showing no change in $R^2$ and $EVS$ for deflection, due to its constrained potential for enhancement. Overall, integrating feature engineering with LSTM-attention model provides a robust approach for accurate structural response prediction and offers valuable insights for future structural health monitoring applications.