The resilient modulus (M_R) of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design. In order to determine the resilient modulus of compacted subgrade soils quickly and accurately, an optimized artificial neural network (ANN) approach based on the multi-population genetic algorithm (MPGA) was proposed in this study. The MPGA overcomes the problems of the traditional ANN such as low efficiency, local optimum and over-fitting. The developed optimized ANN method consists of ten input variables, twenty-one hidden neurons, and one output variable. The physical properties (liquid limit, plastic limit, plasticity index, 0.075 mm passing percentage, maximum dry density, optimum moisture content), state variables (degree of compaction, moisture content) and stress variables (confining pressure, deviatoric stress) of subgrade soils were selected as input variables. The M_R was directly used as the output variable. Then, adopting a large amount of experimental data from existing literature, the developed optimized ANN method was compared with the existing representative estimation methods. The results show that the developed optimized ANN method has the advantages of fast speed, strong generalization ability and good accuracy in M_R estimation.

弹性模量（M _R）路基土壤通常用于表征路基的刚度，并且是路面设计中的关键参数。为了快速，准确地确定压实路基土的弹性模量，提出了一种基于多种群遗传算法（MPGA）的优化人工神经网络（ANN）方法。MPGA克服了传统人工神经网络的低效率，局部最优和过度拟合等问题。开发的优化的ANN方法由十个输入变量，二十一个隐藏的神经元和一个输出变量组成。物理性质（液体极限，塑性极限，可塑性指数，0.075毫米通过百分比，最大干密度，最佳含水量），状态变量（压实度，含水量）和应力变量（约束压力，选择路基土的偏应力作为输入变量。这M _R直接用作输出变量。然后，利用现有文献中的大量实验数据，将开发的优化的人工神经网络方法与现有的代表性估计方法进行比较。结果表明，所开发的优化的人工神经网络方法具有速度快，泛化能力强，M _R估计精度高的优点。