Optimization design and prediction of the snow-melting pavement based on electrical-thermal system

• Published in: Cold regions science and technology Vol. 193; p. 103406
• Main Authors: Jiao, Wenxiu, Sha, Aimin, Liu, Zhuangzhuang, Li, Wei, Zhang, Lei, Jiang, Shuzhou
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2022
• Subjects: Back-propagation neural network; Design parameters; Electrical-thermal system; Heating wire; Snow melting efficiency; Back-propagation neural network; Design parameters; Electrical-thermal system; Heating wire; Snow melting efficiency
• ISSN: 0165-232X; 1872-7441
• DOI: 10.1016/j.coldregions.2021.103406

Electrically heated roads are used as a substitute for chemicals and mechanical devices to ensure mobility and traffic safety in winter weather. This paper aims to design an electrical-thermal system and recommends the best system parameters based on the melting efficiency, energy costs and mechanical durability of the pavement. In addition, the snow melting effect was verified experimentally according to the recommended parameters and the accuracy of the electrical-thermal system was predicted and evaluated by back-propagation neural network. Experiment results indicate that the 24 K Teflon‑carbon fiber heating wire is suitable for the road heating system. It can provide a stable heat source and has the characteristics of high temperature/pressure resistance during the asphalt mixture molding and rolling process. The system parameters were recommended based on snow melting efficiency and mechanical durability of the pavement such as temperature stress and temperature uniformity. The optimal heating wire spacing is 10 cm, set heating wire depth as 4 cm, besides, the heating wire power can be determined based on energy consumption, and actual environmental conditions. The recommended parameters were used for snow melting laboratory experiments to verify the validity of the electrical-thermal system. Finally, the temperature trend of this system can be predicted and evaluated well with high accuracy by the back-propagation neural network. The research enriches the theory of thermodynamic structural design for snow melting pavement systems. •The optimal electrical-thermal system parameters are recommended.•The snow melting effect was verified experimentally according to the recommended parameters.•The accuracy of the electrical-thermal system was predicted and evaluated by back-propagation neural network.