Publication Date

1-13-2026

Document Type

Article

Publication Title

Processes

Volume

14

Issue

2

DOI

10.3390/pr14020294

Abstract

To address traffic safety hazards from asphalt pavement icing in Xinjiang’s cold regions and inefficiencies of conventional deicing and imprecise geothermal deicing systems, this study focused on local asphalt surfaces. Using “outdoor qualitative screening and indoor quantitative verification”, key variables were identified via controlled tests and their coupling effects on the time to complete icing were quantified through an L16(44) orthogonal test (a 4-factor, 4-level design encompassing 16 test groups). A Backpropagation (BP) neural network model (3 inputs, 5 hidden neurons, and a learning rate of 0.7) optimized with 64 datasets was established to predict the time to complete icing of asphalt pavements, achieving a prediction accuracy (PA) of 90.7% for the time to complete icing and a mean error of merely 0.71 min. Dynamic icing risk thresholds (high/medium/low) were established via K-means clustering and statistical tests, enabling data-driven precise activation and on-demand regulation of geothermal deicing systems. This resolves energy waste and deicing delays, offering technical support for efficient geothermal utilization in cold-region transportation infrastructure, and provides a scalable “factor screening + model prediction” framework for asphalt pavement anti-icing practice.

Funding Number

2024A01005-1

Funding Sponsor

Science and Technology Bureau of Xinjiang Production and Construction Corps

Keywords

asphalt pavement icing, BP neural network, dynamic decision threshold, geothermal ice-melting system, influencing variables of ice formation

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Aviation and Technology

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