Precise prediction of asphalt pavement temperature variations is critical for optimizing design and maintenance strategies, given their profound influence on material stiffness, strength, and fatigue resistance. Traditional forecasting methods, however, often struggle with the inherent time-dependent complexity of these temperature profiles. This study proposes a novel hybrid deep learning model, integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, to precisely predict daily maximum and minimum asphalt pavement temperatures at various depths using real-time meteorological data. By synergistically combining LSTM's capacity for long-term dependency modeling with GRU's efficiency in capturing short-term patterns and mitigating individual drawbacks like LSTM's overfitting and GRU's reduced complexity for intricate sequences, the LSTM-GRU architecture significantly enhances predictive accuracy. Rigorously trained and validated using an extensive dataset from May 2018 to March 2023, the hybrid model consistently outperformed a comprehensive suite of baseline models, achieving superior predictive accuracy with average R² values of 0.95 for maximum temperatures and 0.96 for minimum temperatures, along with notable improvements across MAE, MSE, and RMSE. Furthermore, SHapley Additive exPlanations (SHAP) analysis provided critical interpretability, confirming atmospheric and surface temperatures as the most influential predictors, followed by wind speed, relative humidity, and precipitation. These findings unequivocally demonstrate the exceptional robustness, accuracy, and interpretability of the LSTM-GRU hybrid, offering a promising and advanced solution vital for informed pavement engineering decisions and proactive management strategies.
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