This study aimed to explore the influence of material design parameters on the physical and mechanical properties of recycled asphalt. A Box–Behnken design was employed to determine the optimal preparation scheme for 17 groups of recycled asphalt. The effects of styreneic methyl copolymer (SMC) regenerant content, styrene–butadiene–styrene (SBS)-modified asphalt content, and shear temperature on the mechanical properties of recycled asphalt were analyzed using conventional and high/low-temperature rheological tests. The optimal processing parameters were determined by a response surface model based on multiple response indexes. The results revealed that the SBS-modified asphalt content had the most significant effect on the penetration of recycled asphalt. An increase in SMC regenerant content led to a gradual decrease in the rutting factor, while SBS-modified asphalt content had the opposite effect. The usage of SMC regenerant helped to reduce non-recoverable creep compliance by adjusting the proportion of viscoelastic–plastic components in recycled asphalt. Furthermore, the stiffness modulus results indicated that the addition of SMC regenerant improved the recovery performance of recycled asphalt at a low temperature. The recommended contents of SMC regenerant and SBS-modified asphalt are 7.88% and 150%, respectively, with a shear temperature of 157.7 °C.

The evaluation of in-service pavements’ performance is a complex system that encompasses a variety of uncertain factors. These uncertainties include random, fuzzy, gray, and unascertained information, and their interrelationships are intricate, making comprehensive quantification unachievable. Nonetheless, current highway management organizations rely on a comprehensive indicator, namely, the Pavement Quality Index (PQI), to assess the level of pavement performance. This paper introduces a novel approach that employs blind number theory to evaluate the reliability of pavement performance test data. The proposed method aims to enhance the representativeness of PQI and is demonstrated using detection data from highway asphalt pavements in Hunan Province. The method takes into account the probability distribution characteristics of evaluation metrics and incorporates the blind number representation format of PQI. A confidence model for pavement performance evaluation is established to assess the reliability of pavement detection results. The method also integrates expert empowerment and entropy weight to consider both the subjectivity of evaluation and the objectivity of measured data. The method presented in this study has demonstrated superior performance compared to traditional evaluation index systems. This is attributed to the effective utilization of blind information to accurately characterize the discreteness of pavement performance indexes. Consequently, pavement performance can be quantitatively graded based on anticipated issues and data.