Multi-objective optimization of cold mix materials based on response surface methodology
Wen Xu, Yasir Ibrahim Shah, Sheng Xu, Sicheng Wang, Kai Zhang, Xiangyang Fan, Bin Liu
Abstract
Designing cold mix and cold-laid asphalt mixtures based on performance criteria holds significant promise for enhancing road surface quality. This study specifically targets the optimization of basalt fiber cement composite modified cold mix asphalt mixtures to elevate their performance characteristics. Employing the Box Behnken method within a response surface methodology framework, we orchestrated a meticulously designed three-factor, three-level experiment. Emulsified asphalt , basalt fiber, and cement content were delineated as key influencing factors, while performance metrics encompassing high-temperature performance (including irreparable creep flexibility and strain recovery rate), bonding performance (flying loss), and low-temperature performance (splitting tensile strength) were designated as response variables to scrutinize the effects of these factors on cold mix asphalt mixture performance. Utilizing the experimental response values, we constructed a response surface model and employed multiple regression equations to accurately capture the functional relationships. Subsequently, a multivariate analysis of variance was conducted to ascertain the selection conditions for each response variable, leading to the optimization of the dosage of various influencing factors. Finally, the predicted values were validated against measured data. Our findings underscore the effectiveness of the quadratic equation model in accurately capturing the interplay between the influencing factors and the high-temperature, bonding, and low-temperature performance of cold mix asphalt mixtures. Furthermore, the fitting models for each response variable exhibit a high degree of significance and goodness of fit across different grading conditions, underscoring the robustness of our approach.