Most chemical stabilization guidelines for subgrade/base use unconfined compressive strength (UCS) of treated soils as the primary acceptance criteria for selecting optimum stabilizer in laboratory testing. Establishing optimal additive content to augment UCS involves a resource-intensive trial-and-error procedure. Also, samples collected from discrete locations for laboratory trials may not be representative of the overall site. This study aims to minimize the number of laboratory trials and help strategize sampling locations by developing spatial maps of UCS at different treatment levels for lime and cement. These spatial maps were developed using machine-learning techniques, and using a database compiled from various reported studies on lime and cement stabilization of soils in the United States. Supervised learning methods under regression and classification categories were used to quantify and classify UCS values after treatments, respectively. Commonly available soil properties like Atterberg limits, gradation, and organic contents along with treatment type and amount were used as predictors and UCS values as the response. Median R² for the best regression model was 0.75 for lime and 0.82 for cement, while the Correct Prediction Rate (CPR) for the best classification model was 92% for lime and 80% for cement. Results showed that satisfactory predictions could be made regarding stabilizer effectiveness using simple soil information commonly available. Best performing models for cement treatment were selected for generating the spatial maps for two counties in Montana. Soil samples collected from these counties were tested with different cement contents to verify the predictions. The results indicate that the Pearson’s correlation coefficient for the regression model was 0.78 and CPR for the classification model was 92%. The authors hope that this study and future studies like these will increase data-driven-decision-making in geotechnical engineering practices.

大多数用于路基/地基的化学稳定准则都将处理过的土壤的无侧限抗压强度（UCS）作为在实验室测试中选择最佳稳定剂的主要接受标准。建立最佳添加剂含量以增强UCS涉及资源密集的反复试验程序。同样，从不连续地点收集的用于实验室试验的样品可能不代表整个地点。这项研究旨在通过开发石灰和水泥在不同处理水平下的UCS空间图来最大程度地减少实验室试验的数量，并帮助制定采样位置的策略。这些空间图是使用机器学习技术开发的，并使用了根据对美国石灰和水泥稳定土壤的各种报道研究而汇编的数据库。回归和分类类别下的监督学习方法分别用于对治疗后的UCS值进行量化和分类。常用的土壤性质（如阿特伯格限值，等级和有机物含量以及处理类型和用量）用作预测指标，而UCS值用作响应指标。中位数R²最佳回归模型的石灰预测值为0.75，水泥为0.82，而最佳分类模型的正确预测率（CPR）为石灰为92％，水泥为80％。结果表明，使用常用的简单土壤信息可以对稳定剂的有效性做出令人满意的预测。选择了性能最佳的水泥处理模型，以生成蒙大拿州两个县的空间图。从这些县收集的土壤样品用不同的水泥含量进行了测试，以验证预测结果。结果表明，回归模型的皮尔逊相关系数为0.78，分类模型的CPR为92％。作者希望这项研究以及类似的未来研究能够增加岩土工程实践中数据驱动的决策制定。