This paper presents the results of using rigorous modeling artificial neural network and genetic algorithm to examine the proper stabilization of very weak subgrade soils at high moisture contents. The experimental database was performed in Louisiana transportation research center for four types of soft soil, 125 samples data were prepared and used in development ANN and genetic algorithm models. For two models, the input variables include eight parameters, namely cement percentage, lime percentage, PI, silt percentage, fly ash, optimum moisture content OMC, moisture content M.C, and clay percentage, the output variable includes resilient modulus for different types of stabilized subgrade. Furthermore, mathematical models were proposed to predict the resilient modulus for stabilized weak subgrade with different types of stabilizer agent such as cement, lime, and fly ash with four different subgrade soil types of different plasticity indices. Besides, the proposed models for estimating resilient modulus for stabilized subgrade were derived by an artificial neural network model and genetic algorithm. The scheme method displayed is a particular process of which resilient modulus for stabilized subgrade can be determined directly. The results show impressive due to obtain a high value for regression for sets of models; we obtained another accurate result for M_r by using Gene expression programming. Following the model design is stablished; the powers and deficiencies of the proposed models are tested by matching the resilient modulus proposed from two models with the resilient modulus extracted from experimental test concerning the R² values. Further, in the neural network model, an exact assessment was achieved using r2 = 0.97. Genetic algorithm with a coefficient of determination (R²) of 0.95 to determine the resilient modulus of stabilized subgrade. Achievement estimation of the ANN and genetic algorithm pointed out that the theses methods were capable to predict resilient modulus of stabilized with powerful and higher efficiency and outcomes of these models was more conventional to the experimental results. Finally, sensitivity analysis of the achieved models has been performed to examine the impact of input variables on output (M_r) and determines that the cement percentage, lime percentage, fly ash percentage, PI, clay percentage, MC, OMC, and silt percentage are the powerful variables on the resilient modulus of stabilized subgrade.

本文介绍了使用严格的建模人工神经网络和遗传算法来检验高水分含量的极弱路基土壤的适当稳定性的结果。该实验数据库是在路易斯安那州交通研究中心针对四种类型的软土进行的，准备了125个样本数据，并用于开发ANN和遗传算法模型。对于两个模型，输入变量包括八个参数，即水泥百分比，石灰百分比，PI，粉砂百分比，粉煤灰，最佳含水量OMC，含水量MC和黏土百分比，输出变量包括不同类型的稳定剂的弹性模量路基。此外，提出了数学模型来预测水泥，石灰和粉煤灰等具有不同可塑性指数的四种不同路基土壤类型的稳定剂对稳定的弱路基的弹性模量。此外，通过人工神经网络模型和遗传算法推导了所提出的稳定路基弹性模量的估算模型。显示的方案方法是一个特定过程，可以直接确定稳定路基的弹性模量。结果显示出令人印象深刻，这是因为获得了较高的模型集回归值；我们获得了M的另一个准确结果 利用人工神经网络模型和遗传算法推导了稳定路基弹性模量的模型。显示的方案方法是一个特定过程，可以直接确定稳定路基的弹性模量。结果显示出令人印象深刻，这是因为获得了较高的模型集回归值；我们获得了M的另一个准确结果 利用人工神经网络模型和遗传算法推导了稳定路基弹性模量的模型。显示的方案方法是一个特定过程，可以直接确定稳定路基的弹性模量。结果显示出令人印象深刻，这是因为获得了较高的模型集回归值；我们获得了M的另一个准确结果_ř通过使用基因表达编程。遵循模型设计；通过将两个模型提出的弹性模量与从实验测试中提取的有关R ²值的弹性模量进行匹配，来测试所提出模型的功效和不足。此外，在神经网络模型中，使用r2 = 0.97可获得精确的评估。具有确定系数的遗传算法（R ²）0.95来确定稳定路基的弹性模量。人工神经网络的成就估计和遗传算法指出，这些方法能够有效，高效地预测稳定的弹性模量，并且这些模型的结果对实验结果更为常规。最后，对获得的模型进行敏感性分析，以检查输入变量对产出（M _r）的影响，并确定水泥百分比，石灰百分比，粉煤灰百分比，PI，粘土百分比，MC，OMC和淤泥百分比是稳定路基回弹模量的有力变量。