PURPOSES : In this study, energy-consuming processes in asphalt plants were evaluated, and the drying and mixing processes were characterized using a thermal equilibrium equation-based model to quantitatively estimate the amount of energy consumed during the production of mixtures in asphalt concrete plants. METHODS : An energy consumption model based on the thermal equilibrium equation was used to estimate the energy consumption of the aggregate drying process that consumes the maximum energy; the energy consumed for material transportation, storage, and operation of other facilities was cited from the literature. The results were compared with the actual results obtained for recycled hot asphalt mixtures and recycled warm mix asphalt mixtures, and a sensitivity analysis was performed by varying the conditions. RESULTS : An analysis of the main processes required to produce asphalt mixtures showed that the water content had the largest impact on energy consumption (approximately 80%). This quantitatively supports the opinion of field practitioners that maximum energy is consumed during aggregate drying. Although some discrepancies were observed, the results were found to be reasonable and within the range of typical measurements. CONCLUSIONS : The thermal energy consumption estimation model provides consistent results that reflect the characteristics of the mixture and can be used to derive the thermal energy consumption rates for individual materials, such as aggregates and binders. This can be used to identify the priorities for process optimization within a plant.

PURPOSES : This study aims to determine whether machine learning techniques based on the results of chemical analysis experiments can be rationally applied to evaluate the aging of various asphalt binders used throughout the country. METHODS : We conducted chemical experiments such as FT-IR, H-NMR, C- NMR, and GPC for the three-stage aging levels of eight types of asphalt binders used in the country and utilized two artificial neural network models to determine valid chemical experimentation and conditions for the use of neural modeling through predictions. RESULTS : The M-prop model, which combined the findings from each neural network model into a single artificial neural network model, demonstrated superior predictive performance compared with the M-base model, which assessed aging using two cluster layers. In addition, the minimum amount of data required to achieve 100% predictive accuracy for the target asphalt binders, regardless of the artificial neural network model, was 18, and the amount of training data decreased to less than 50%. CONCLUSIONS : The predictive accuracy of the aging of asphalt binders was significantly enhanced when GPC data was used, indicating that GPC should be prioritized in evaluating the aging of asphalt binders.