基于介电特性的沥青混合料密度预估优化模型

为了提高沥青路面传统密度预估模型精度，开展了沥青混合料密度预估模型参数优化研究。基于混合材料的介电混合模型，引入散射体影响系数v和形状因子u，推导出沥青混合料密度预估理论模型的通用公式；采用Percometer介电常数仪测量不同类型沥青混合料试件及其组分（沥青和矿料）的相对介电常数，基于麦夸特法和通用全局优化算法，求解通用公式密度预估值与表干法实测密度值的误差最小值，确定了ISO（Influence Coefficient and Shape Factor Optimization）模型的v和u最优解，并对ISO模型进行了可靠性SW检验；最后通过不同密度预估模型对比分析，验证了ISO模型密度预估的优越性。研究结果表明：ISO模型的v和u值分别为5.1、-4.5，采用ISO模型预估沥青混合料密度有效可靠；密级配沥青混合料的公称最大粒径越小，ISO模型密度预估值与实测密度值的拟合优度越高，平均相对误差越小；相对瑞雷模型和ALL模型，ISO模型密度的预估效果最好，能显著提高空隙率偏大沥青混凝土的密度预估精度。研究为更加准确地预估沥青路面密度提供了新方法。

Optimization Model of Asphalt Mixture Density Prediction Based on Dielectric Property

To improve the accuracy of the traditional density prediction model for asphalt pavement, a parameter optimization of the asphalt mixture density prediction model was studied. Based on the dielectric mixing model of the asphalt mixture, the general formula of the asphalt mixture density prediction theory model was derived by introducing the scatterer influence coefficient v and shape factor u. The relative permittivities of different asphalt mixture samples and their components (asphalt and aggregate) were measured by Percometer tester. Based on the Levenberg-marquardt method and the Universal Global Optimization algorithm, the minimum error between the density estimated by the general formula and the value measured by the surface dry method was solved, which gavethe v and u optimal solutions of the influence coefficient and shape factor optimization (ISO) model.The reliability of the ISO model was tested using the Shapiro-wilk (SW) method. Finally, the superiority ofthe ISO model was verified by comparing different density prediction models. The research results show that the v and u values of ISO model are 5.1 and -4.5, respectively. It is effective and reliable to use ISO model to estimate the density of asphalt mixture. The smaller the nominal maximum particle size of the dense-graded asphalt mixture is, the higher the goodness of fit between the density predicted by the ISO model and the measured value, and the average relative erroris on the contrary. Compared with the Rayleigh and the Al-qadi, Lahouarand Leng(ALL) models, the ISO model offers the best density prediction, which can significantly improve the density prediction accuracy of asphalt concrete with large porosity. This study provides a new method for more accurate prediction of asphalt pavement density.