This study aims at developing a gene expression programming (GEP) model and an artificial neural network (ANN) model to predict the resilient modulus (M_R) of compacted pavement subgrade soils based on their physical properties, external stress states, and environmental factors. A database of 2813 M_R measurements derived from 12 subgrade soils with different moisture-temperature histories was established for model development and validation. Influencing factors considered in this database include the weighted plasticity index (wPI), dry unit weight (γ_d), confining stress (σ_c), deviator stress (σ_d), moisture content (w), and the number of freeze-thaw cycles (N_FT). Sensitivity analysis was conducted to evaluate the importance of each factor. The order of influencing factors by decreasing importance was found to be wPI, γ_d, w, N_FT, σ_d, σ_c and the importance of the σ_c and σ_d is remarkably lower than other factors. This indicates that the M_R of compacted subgrade soils is strongly dependent on the soil type (wPI, γ_d) and is more sensitive to environmental factors (N_FT, w) than external stress states (σ_c, σ_d). The developed GEP and ANN models reasonably predicted the M_R in the database and achieved better performance compared to several existing empirical models.

这项研究旨在开发基因表达编程（GEP）模型和人工神经网络（ANN）模型，以基于夯实路面路基土壤的物理性质，外部应力状态和环境因素来预测其弹性模量（M _R）。建立了一个数据库，该数据库包含来自12个具有不同水分温度历史的路基土壤的2813 M _R测量值，用于模型开发和验证。在此数据库中考虑的影响因素包括加权塑性指数（WPI），干单位重量（γ _d），侧限应力（σ _Ç），偏应力（σ _d），水分含量（瓦特），以及冻融循环的次数（N _FT）。进行敏感性分析以评估每个因素的重要性。影响通过降低重要性因子的顺序被发现是WPI，γ _d，瓦特，N _FT，σ _d，σ _Ç和的重要性σ _Ç和σ _d比其他因素显着地降低。这表明，中号_ř压实路基土的强烈依赖于土壤类型（WPI，γ _d），并且是环境因素（N更敏感_FT，瓦特）比外部应力状态（σ _Ç，σ _d）。与几个现有的经验模型相比，已开发的GEP和ANN模型可以合理地预测数据库中的M _R并获得更好的性能。