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.

PURPOSES : A model for minimizing cutting loss and determining the optimum layout of blocks in pavements was developed in this study. METHODS : Based on literature review, a model which included constraints such as the amount, volume, overlap, and pattern, was developed to minimize the cutting loss in an irregular pavement shape. The Stach bond, stretcher bond, and herringbone patterns were used in this model. The harmony search and particle swarm algorithms were then used to solve this model. RESULTS : Based on the results of the model and algorithms, the harmony search algorithm yielded better results because of its fast computation time. Moreover, compared to the sample pavement area, it reduced the cutting loss by 20.91%. CONCLUSIONS : The model and algorithms successfully optimized the layout of the pavement and they have potential applications in industries, such as tiling, panels, and textiles.

PURPOSES : The objective of this study is to understand blow-up distress and causes in concrete pavement. METHODS : Feasible causes of blow-up and existing models were reviewed based on the literature. Three analytical models were adopted to perform a sensitivity analysis. Input parameters reflected the typical concrete pavement of national expressways. Evaluation of blow-up models was based on the amount of temperature increase and zero stress temperature of the concrete pavement. RESULTS : A review of the literature indicated that the five major causes of blow-up were: increase in temperature and solar radiation, alkaliaggregate reaction (AAR), friction characteristics between the concrete slab and subbase, joint closure (incompressible), and joint freezing. The sensitivity analysis revealed that the coefficient of thermal expansion had the greatest influence on the blow-up safety temperature. CONCLUSIONS : From existing blow-up model results, it could be concluded that the construction of concrete pavement during the winter season was not effective at preventing blow-up. In addition, an equivalent coefficient of thermal expansion that considers slab expansion due to AAR was proposed as a model input parameter for concrete pavement sections damaged by AAR.