아스팔트 포장 결빙 발생 예측을 위한 열전달 모형 및 판단 기준 연구 (Analysis of Heat-Transfer Modeling and Decision Criteria for Predicting Black Ice on Asphalt Pavements)

서지정보

· 발행기관 : 한국도로학회
· 수록지 정보 : 한국도로학회논문집 / 27권 / 1호 / 25 ~ 34페이지
· 저자명 : 노승현, 윤태영

초록

This paper presents a finite-difference method (FDM)-based heat-transfer model for predicting black-ice formation on asphalt pavements and establishes decision criteria using only meteorological data. Black ice is a major cause of winter road accidents and forms under specific surface temperature and moisture conditions; however, its accurate prediction remains challenging owing to dynamic environmental interactions. The FDM incorporates thermodynamic properties, initial pavement-temperature profiles, and surface heat-transfer mechanisms, i.e., radiation, convection, and conduction. Sensitivity analysis shows the necessity of a 28-d stabilization period for reliable winter predictions. Black-ice prediction logic evaluates the surface conditions, relative humidity, wind speed, and latent-heat accumulation to assess phase changes. Field data from Nonsancheon Bridge were used for validation, where a maximum prediction accuracy of 64% is indicated in specific cases despite the overestimation of surface temperatures compared with sensor measurements. These findings highlight the challenges posed by wet surface conditions and prolonged latent-heat retention, which extend the predicted freezing duration. This study provides a theoretically grounded methodology for predicting black ice on various road structures without necessitating additional measurements. Future studies shall focus on enhancing the model by integrating vehicle-induced heat effects, solar radiation, and improved weather-prediction data while comparing the FDM with machine-learning approaches for performance optimization. The results of this study offer a foundation for developing efficient road-safety measures during winter.

영어초록

This paper presents a finite-difference method (FDM)-based heat-transfer model for predicting black-ice formation on asphalt pavements and establishes decision criteria using only meteorological data. Black ice is a major cause of winter road accidents and forms under specific surface temperature and moisture conditions; however, its accurate prediction remains challenging owing to dynamic environmental interactions. The FDM incorporates thermodynamic properties, initial pavement-temperature profiles, and surface heat-transfer mechanisms, i.e., radiation, convection, and conduction. Sensitivity analysis shows the necessity of a 28-d stabilization period for reliable winter predictions. Black-ice prediction logic evaluates the surface conditions, relative humidity, wind speed, and latent-heat accumulation to assess phase changes. Field data from Nonsancheon Bridge were used for validation, where a maximum prediction accuracy of 64% is indicated in specific cases despite the overestimation of surface temperatures compared with sensor measurements. These findings highlight the challenges posed by wet surface conditions and prolonged latent-heat retention, which extend the predicted freezing duration. This study provides a theoretically grounded methodology for predicting black ice on various road structures without necessitating additional measurements. Future studies shall focus on enhancing the model by integrating vehicle-induced heat effects, solar radiation, and improved weather-prediction data while comparing the FDM with machine-learning approaches for performance optimization. The results of this study offer a foundation for developing efficient road-safety measures during winter.

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