基于分形维数及考虑假集料影响的冷再生混合料级配控制方法

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

摘要:

为了更好控制混合料级配，以获取最佳路用性能，对冷再生混合料级配分形特征以及沥青路面回收旧料（RAP）假集料特性开展了研究。首先选取了规范级配范围内6种代表性级配曲线，借助分形几何理论，建立了粒径分布函数，得到了合成级配分形维数（D）、粗集料级配分形维数（D_C）及细集料级配分形维数（D_F），并分析了三者与冷再生混合料干劈裂强度、冻融劈裂强度比、动稳定度和最大弯拉应变的相关性，然后建立了基于级配分形维数的冷再生混合料路用性能预估模型，最后通过分析级配变异性对冷再生混合料性能的影响，提出了考虑假集料影响的冷再生混合料级配控制方法。结果表明：分形维数可以定量描述冷再生混合料粒径分布及级配走向，进而反映冷再生混合料各项路用性能，基于分形维数的性能预估模型可以较好预测冷再生混合料各项路用性能，预估值与实测值相对误差基本在15 %以内；当冷再生混合料“抽提前后级配变化率方差”（D_V）大于85或“抽提前后级配变化值”（V_a）大于3.6时，宜通过添加新粗集料或调整级配曲线靠近下限等措施来提升冷再生混合料高温抗力。

关键词:分形维数;假集料;冷再生混合料;级配;预估模型;路用性能

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