Recycled concrete aggregates (RCAs) have low quality when compared with natural or conventional aggregates as the paste adhering to it is the key aspect that affects its functionality. Since the adhering cementitious paste weakens the adhesion between the aggregate and the binder, it becomes a decisive factor in the mechanical behavior of the asphalt mixture. It turns out that enhancing the surface of the aggregate or eliminating the paste attached to the NA (natural aggregate) is crucial for improving interfacial bonding. Therefore, the treatment and evaluation of the RCAs by laboratory testing method were studied in this research to assess their reuse in the asphalt pavement surface coarse layer. With the various techniques for removing paste from the RCA, a combination of three enhancement processes were developed for the effective removal of the cement paste, which are mechanical, chemical, and 400 °C conditioning thermal treatment. The RCAs were first charged in a Los Angeles machine for the mechanical treatment to remove parts of the attached cement. Then they were soaked in two types of acids, namely hydrochloric and sulfuric, with different concentrations to determine the most effective or optimum molarity for about a 48 h soaking duration. Then a 2 h thermal treatment was conducted on the RCA samples. After all the treatments were done, the RCA aggregates were subjected to different types of tests to examine their properties in order to ensure their full potential in terms of their physical, chemical, mineralogical, and surface microstructure characteristics. Based on the experiment design, the study intends to examine the quality of the treated recycled aggregates generated by the combination approach as well as to investigate the optimal acid concentration and type. The results show that the proposed mechanochemical and thermal treatment reinforced the quality of the RCAs when compared to the non-treated samples. Meanwhile, HCl proved to be the optimum compared to H₂SO₄ in most examinations of the properties. In the end, better chemical qualities were validated, and the RCA qualities were improved.

Natural resources are the most critical material basis for national economic and social development, and the key guarantee for improving national viability. With the development of national economy and the progress of society, people’s awareness of the importance of resources is also growing, and the issue of resources has begun to become an important topic of common concern for governments around the world. At a time when non-renewable resources such as coal and natural gas are in urgent need, and energy shortage has gradually become a problem endangering global economic development, more and more countries have begun to actively implement the “sunshine plan”, that is, to develop and utilize sunlight resources to find new impetus for economic and social development. Solar energy resource is a very important resource, which is inexhaustible, non-polluting, and relatively cheap and can be used at will. Vigorously developing and using solar energy is the most effective way to solve the shortage of resources and achieve sustainable economic development. Therefore, the application in the highway field is very necessary to promote the construction of distributed photovoltaic power generation system.

Traditional centralized highway energy systems exhibit significant resilience shortcomings in the face of climate change mitigation requirements and increasingly frequent extreme weather events. Meanwhile, prevailing microgrid control strategies remain predominantly focused on economic optimization under normal conditions, lacking the flexibility to address dynamic risks or the interdependencies between transportation and power systems. This study proposes an adaptive, risk-driven control framework that holistically coordinates power generation infrastructures, microgrids, demand-side loads, energy storage systems, and transport dynamics through continuous risk assessment. This enables the system to dynamically shift operational priorities—from cost-efficiency in stable periods to robustness during emergencies. A multi-objective optimization model is established, integrating infrastructure resilience, operational costs, and traffic impacts. It is solved using an enhanced evolutionary algorithm that combines the non-dominated sorting genetic algorithm II with differential evolution (NSGA-II-DE). Extensive simulations under extreme weather scenarios validate the framework’s ability to autonomously reconfigure operations, achieving 92.5% renewable energy utilization under low-risk conditions while elevating critical load assurance to 98.8% under high-risk scenarios. This strategy provides both theoretical and technical guarantees for securing highway renewable energy system operations.