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A finite element simulation method for fatigue damage calculation and life prediction of pavement structures under the influence of temperature and traffic distribution factors was proposed in this study. Relying on the test cold recycled asphalt pavement structure, the existing distress, pavement structure forms, field monitored temperature and strain were first introduced and analyzed. Then, in constructing the numerical model, the viscoelastic constitutive model was introduced to characterize the effect of temperature and loading conditions on the mechanical response of the hot mix asphalt (HMA) layers and the emulsified asphalt cold recycled (EACR) layer. The damage variables are defined by fatigue equations, and the damage accumulation can be determined by Miner's linear fatigue accumulation theory. To reflect the distribution of traffic volume, the total traffic volume of a year was divided into 144 axle load groups according to the monthly and hourly distribution conditions. Accordingly, based on the monthly maximum and minimum temperature, 12 representative days were selected to represent the climate characteristics of 12 months, respectively. Then, each representative day's measured structural temperature data were extracted every 2 hours and linearly interpolated to obtain 144 representative temperature fields corresponding to 144 axle load groups. Through the above method, simulation calculations were performed for cold recycled asphalt pavement structures with different cement-stabilized aggregate (CSA) base stiffnesses. The results show that the fatigue damage accumulation of the EACR layers reaches its highest value in winter and midday hours, owing to the temperature variation and traffic distribution. Due to the weak fatigue resistance of EACR mixtures, it is not recommended to be paved EACR layers directly on top of the CSA base with poor bearing capacity. Otherwise, fatigue cracking is likely to occur first. For this reason, recommendations for ensuring the durability of the cold recycled pavement structure were also proposed in the study. [ABSTRACT FROM AUTHOR]

This paper used the finite element method to analyse surface water distribution characteristics on porous asphalt layer under unsaturated seepage, and further compared the critical rainfall intensity of porous asphalt pavements with different design parameters. In this study, the Richards equation was used to describe the unsaturated seepage, and the results were validated with experimental data. The results show that when the water accumulates on the pavement surface, it would firstly emerge at the lowest level of pavement along the longitudinal direction, and then spread along transverse and longitudinal directions. The water depth has no remarkable difference along the longitudinal direction while it differs a lot among different transverse positions. The distribution of surface water depth was analysed and the correlation between the surface water coverage in the transverse direction and the rainfall intensity was established with high R-square values. For pavements with different geometries, the porous layer thickness has the most remarkable effect on the critical rainfall intensity of porous asphalt pavements. [ABSTRACT FROM AUTHOR]

The routine maintenance and rehabilitation of road pavement is vital to keep up with the service level and bearing capacity. However, the current problem is that, with the continuous increase of the scale of maintenance data, the traditional decision-making cannot satisfy the requirements in terms of accuracy and efficiency. The objectives of this paper were to improve the accuracy and efficiency of the maintenance decisions, overcome the decision error caused by insufficient human experience, and develop the mapping process for decision plans. This paper presented a decision-making method for asphalt pavement maintenance using improved weight random forest algorithm (IWRF) based on the correlation analysis (CA) and the analytic hierarchy process (AHP). Firstly, appropriate features were selected through CA of road detection data, and then decision trees were constructed based on Bootstrapping. Finally, qualified decision trees were chosen and weighted by AHP to form a random forest. To examine the feasibility, the algorithm was applied in a maintenance decision of the 80 km highway in Jiangsu province. The results showed that IWRF had a decision-making accuracy of up to 90%. Comparing with the traditional random forest algorithm, the IWRF algorithm had a 4.35% higher accuracy and saved 75% computation time. [ABSTRACT FROM AUTHOR]