فهرست مطالب jianying hu

At present, the extraction of new bio-asphalt materials from renewable energy sources has become the focus of research in the field of pavement engineering. Bio-asphalt has the characteristics of wide source, low cost, green, pollution-free and is also renewable. With the expansion of the application range of bio-asphalt, its adhesion to aggregates and the healing properties after damage have received extensive attention. In this paper, the bio-asphalt-aggregate adhesion and healing behaviors were evaluated and compared using molecular dynamics approaches. Firstly, the molecular models of vegetable oil bio-asphalt and waste edible oil were established, and the two bio-asphalt molecular models were verified according to the physical quantities such as density, viscosity, cohesive energy density (CED), glass transition temperature and solubility parameter. Then, the bio-asphalt-aggregates interlayer model was established, and the adhesion energy and the energy ratio (ER) value under water conditions were calculated and analyzed using energy theory. A bio-asphalt self-healing model was established, and concentration distribution and diffusion analysis were performed. The results show that the viscosity of bio-asphalt is significantly lower than that of base asphalt, and the shear resistance becomes lower at high temperature. In terms of adhesion, bio-asphalt has better temperature sensitivity. The two bio-asphalts have better adhesion than base asphalt and silica at different temperatures, especially at high temperature (65 °C). There was no significant difference between the adhesion energy of the two bio-asphalts. Bio-asphalt is more affected by water intrusion, and its ability to resist water damage is significantly weaker than that of base asphalt. The NPT density-time curve, concentration distribution and MSD calculation results all showed that the self-healing performance of bio-asphalt was better than that of base asphalt, while the two bio-asphalts showed little difference in healing performance.

During the non-stop construction, risk analysis is essential to ensure airport safety. This study aims to perform risk evaluation of airport safety during the non-stop construction using both Fuzzy Analytical Hierarchy Process (F-AHP) and Bayesian Belief Network (BBN). Risk assessment of airport during non-stop construction involves four risk factors of personnel, equipment, environment, and management. F-AHP is utilized to rank impact of risk factors while BBN is implemented to assess probability of risk occurrence. The combination of F-AHP and BBN is implemented to identify the most significant risk. The results have revealed that environmental factor imposes the most significant influence on risk of airport safety during non-stop construction while equipment factor has the lowest impact on airport safety. The outcomes of this study allow decision makers to manage potential risk and improve airport safety during the non-stop construction.