Pavement freezing depth estimation using hybrid deep-learning models
- Authors
- Roh, Seunghyun; Yami, Yonathan Alemu; Hwang, Hyunsik; Cho, Yoonho
- Issue Date
- Dec-2023
- Publisher
- CANADIAN SCIENCE PUBLISHING
- Keywords
- pavement freezing depth prediction; LSTM; CNN-LSTM; Conv-LSTM
- Citation
- CANADIAN JOURNAL OF CIVIL ENGINEERING, v.51, no.4, pp 423 - 433
- Pages
- 11
- Journal Title
- CANADIAN JOURNAL OF CIVIL ENGINEERING
- Volume
- 51
- Number
- 4
- Start Page
- 423
- End Page
- 433
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/71241
- DOI
- 10.1139/cjce-2023-0131
- ISSN
- 0315-1468
1208-6029
- 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 (CNNLSTM), and convolutional 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 approaches. The proposed approach shows that deep-learning models better estimate the freezing depth of pavements than existing approaches.
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