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.

De-icing agents play a crucial role in winter road maintenance, yet their excessive application can result in pavement deterioration and environmental issues. Existing dosage guidelines lack comprehensive data on the dynamic response of de-icing agents under low-temperature conditions, particularly regarding stage-specific characteristics and multi-factor interactions. This research systematically evaluated the effectiveness of four de-icing agents (NaCl, CaCl₂, MgCl₂, CH₃COOK) within a temperature range of −5 °C to −25 °C, elucidating the two-phase ice-melting process (solid-phase followed by salt solution de-icing) with distinct kinetic mechanisms—a previously underexplored temporal pattern. The study quantified the differential impacts of particle size (small-particle CaCl₂ exhibiting 12% higher efficiency than sheet-like forms), dosage linear correlation, and negligible effects of ice layer thickness and road surface composition, which have not been systematically validated in prior studies. Temperature sensitivity was further refined: NaCl showed a 42.4% efficiency drop between −5 °C and −25 °C, while MgCl₂ maintained stable performance, supporting its potential as an environmentally sustainable alternative. This work provides a quantitative basis for dynamic dosage regulation by integrating stage characteristics and multi-factor optimization, addressing gaps in existing guidelines.

To address high thermal loads and energy costs in Geothermal Road Snow-Melting Systems (GRSSs) within cold regions, this study optimizes energy efficiency through material-level intervention. We developed a composite anti-icing modifier synergistic with low enthalpy geothermal systems, comprising slow-release agents, anti-corrosive components, reinforcing materials, and active chloride salts. By regulating the thermodynamic boundary of the pavement, the freezing point is suppressed to −21 °C. This eliminates the requirement for positive pavement temperatures, significantly reducing the design thermal power. Chloride ion release patterns were analyzed via dissolution and 20-day soaking tests to evaluate structural durability. Results show optimal performance at a 5% modifier dosage and 5.3% bitumen aggregate ratio. Ion release follows a third-order polynomial law and remains stable at 35 °C, ensuring reliability during seasonal thermal cycles. Validation in Xinjiang showed a variation of only 1.5% over 20 days. This research offers an innovative material energy synergy for cascaded geothermal utilization and infrastructure decarbonization in cold regions.