Long short‐term memory‐based real‐time prediction models for freezing depth and thawing time in unbound pavement layers

The prediction of freezing depth and thawing time of unbound pavement layers in cold regions is a critical task in pavement design and management. This study developed long short-term memory (LSTM)-based encoder–decoder models to accurately predict freezing depth and thawing time, with air temperature as the sole input variable. The models, which aim to offer a 14-day advance prediction of the thawing time for effective pavement management, utilized data from the Long-Term Pavement Performance program's database, provided by the Federal Highway Administration in United States. This database contains extensive records on air temperature and freezing states. The LSTM models were trained using data collected from four regions in North America with severely cold winters (Quebec, Minnesota, Ontario, and Maine) and subsequently validated using data from both severely cold (South Dakota and Vermont) and mild (Idaho and Wyoming) winter regions. During the validation phase, the models demonstrated strong performance in the severely cold regions, with predicted freezing depths deviating from the measured values by only 0.05 to 0.20 m and thawing date predictions differing by just 1 to 3 days. However, in the mild winter regions, the models showed less accuracy, with freezing depth differences ranging from 0.10 to 0.40 m and thawing date delays of 3–6 days. Compared to existing analytical and empirical models, the LSTM prediction models developed in this study provide enhanced convenience while maintaining a satisfactory level of accuracy.