The mix design of asphalt concrete is usually accomplished in the Iranian ministry of road and transportation according to the Marshall method. Marshall mix design parameters are a function of grading and properties of aggregates, amount and type of bitumen in asphalt mixtures. Therefore, in order to determine these parameters and the optimum bitumen content, many samples with different compounds and conditions must be manufactured and tested in the laboratory, a process that requires considerable time and cost. Accordingly, the necessity of using new and advanced methods for the design and quality control of asphalt mixtures is becoming more and more evident. Therefore, in this study, a genetic programming simulation method was employed to predict the Marshall mix design parameters of asphalt mixtures. Also, multiple linear regression models were adopted as the base model to evaluate the models presented by the genetic programming method. The models proposed here predict the Marshall mix design parameters based on parameters such as the index of aggregate particle shape and texture, the amount and viscosity of the bitumen. The results demonstrated that the proposed methods are more efficient than the costly laboratory method, and genetic programming models with minimal error (identified in this study with RMSE and MAE parameters) and correlation coefficients > 0.9 can predict relatively accurate Marshall mix design parameters.

沥青混凝土的配合料设计通常是根据马歇尔方法在伊朗道路和交通运输部完成的。马歇尔混合料设计参数取决于沥青混合料的集料的等级和性质，沥青的数量和类型。因此，为了确定这些参数和最佳的沥青含量，必须在实验室中制造和测试许多具有不同化合物和条件的样品，该过程需要大量的时间和成本。因此，使用新的和先进的方法进行沥青混合物的设计和质量控制的必要性变得越来越明显。因此，在这项研究中，采用遗传程序模拟方法来预测沥青混合料的马歇尔混合料设计参数。也，采用多元线性回归模型作为基础模型，对遗传规划方法提出的模型进行评估。这里提出的模型基于诸如聚集颗粒形状和质地的指数，沥青的量和粘度的参数来预测马歇尔混合设计参数。结果表明，所提出的方法比昂贵的实验室方法更有效，具有最小误差（在本研究中使用RMSE和MAE参数确定）且相关系数> 0.9的遗传规划模型可以预测相对准确的Marshall混合设计参数。这里提出的模型基于诸如聚集颗粒形状和质地的指数，沥青的量和粘度的参数来预测马歇尔混合设计参数。结果表明，所提出的方法比昂贵的实验室方法更有效，具有最小误差（在本研究中使用RMSE和MAE参数确定）且相关系数> 0.9的遗传规划模型可以预测相对准确的Marshall混合设计参数。这里提出的模型基于诸如聚集颗粒形状和质地的指数，沥青的量和粘度的参数来预测马歇尔混合设计参数。结果表明，所提出的方法比昂贵的实验室方法更有效，具有最小误差（在本研究中使用RMSE和MAE参数确定）且相关系数> 0.9的遗传规划模型可以预测相对准确的Marshall混合设计参数。