Gradation optimisation for granular-surfaced roads based on the resilient modulus, California Bearing Ratio and plastic strain for blends of recycled and new aggregates

Granular-surfaced roads increasingly experience deterioration from heavy traffic and freeze–thaw cycles, increasing maintenance expenses and safety concerns. Previous studies have investigated how particle size distribution influences engineering properties of granular surfacing materials, focusing primarily on resilient modulus (MR) and California Bearing Ratio (CBR). However, few studies have quantified the effect of gradation to optimize mechanical performance. This study used soaked CBR and repeated load triaxial (RLT) tests to evaluate the influence of gradation on mechanical performance. Materials from two Iowa counties included virgin quarry materials and blends of virgin materials with existing surface materials from in-service roads. Several gradation models were evaluated using the coefficient of determination (R2) and root mean square error (RMSE). The Gompertz model exhibited the best fit and was used to parametrize gradation curves. The parameters were then optimized based on measured soaked CBR, MR, and plastic strain (εp) using weighted sum methods. Results demonstrate that recycled aggregate materials, when blended and optimized using the proposed gradation models, can significantly enhance granular-surfaced road performance. A comprehensive gradation optimization method is proposed, integrating candidate material gradations, parametrization of potential blends using the best-fit model, laboratory soaked CBR and RLT testing, and statistical optimization of gradation parameters.