Pavement Freezing Depth Estimation using Hybrid Deep Learning Models

Abstract

Predicting pavement temperature by depth is crucial for road design, analysis, and maintenance. However, current methods predominantly utilize regression and/or open-form solutions focusing on highways. Additionally, most machine learning models focus on asphalt layers and do not extend to deeper pavement layers. Therefore, this study provides deep-learning models using weather parameters to predict pavement temperature from surface to sublayers and estimate pavement freezing-depth for developing massive apartment complexes. Temperature-by-depth data collected from thin pavements from three locations in South Korea were used. Comparative analyses of Long-short-term-memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), and convolutional-LSTM (Conv-LSTM) were performed. Results showed that CNN-LSTM model performed better with coefficients of determination (R2) of 0.965, 0.987, and 0.981. Additionally, the CNN-LSTM predicted freezing-depth with 0.3%-13.1% error margins outperforming the LSTM, Aldrich's, and Korean Ministry of Transport approach. The proposed approach shows that deep learning models better estimate the freezing depth of pavements than existing approaches.