Gradation Control Method of Cold-Recycled Mixture Considering Influence of Fractal Dimensions and False Aggregates

Abstract:

In order to better control the mixture gradation and obtain the best road performance， the fractal characteristics of the gradation of cold-recycled mixtures （CRM） and the characteristics of reclaimed asphalt pavement（RAP） false aggregates are studied in this paper， which involves the selection of six representative gradation curves within the standard gradation range. Further， the particle size distribution function is established with the help of the fractal geometry theory and the fractal dimension of composite gradation （D）， the fractal dimension of coarse aggregate gradation （D_C）， and the fractal dimension of fine aggregate gradation （D_F） are obtained. The correlation between the fractal dimension and dry splitting strength， freeze-thaw splitting strength ratio（TSR）， dynamic stability， and maximum tensile bending strain of the CRM is analyzed. Subsequently， a road performance prediction model of the CRM based on the fractal dimension of gradation is established. Finally， by analyzing the influence of the variability of the gradation on the performance of the CRM， the gradation control method of the CRM considering the influence of false aggregates is proposed. The results indicate that the particle size distribution and gradation of the CRM can be quantitatively described by the fractal dimension so as to reflect the road performance of the CRM. Based on the performance prediction model of the fractal dimension， the road performance of the CRM can be well predicted， and the relative error between the predicted and measured values is basically within 15 %. When the variance of gradation change rate before and after extraction （D_V） > 85 or gradation change value before and after extraction （V_a） > 3.6， the high-temperature resistance of the CRM should be improved by adding a new coarse aggregate or adjusting the gradation curve closer to the lower limit.

Key words:fractal dimension;false aggregates;cold-recycled mixture;gradation;prediction model;road performance