Predicting the frost depth of soils in pavement design is critical to the sustainability of the pavement because of its mechanical vulnerability to frozen-thawed soil. The reliable prediction of frost depth can be challenging due to the high uncertainty of frost depth and the unavailability of geotechnical properties needed to use the available empirical- and analytical-based equations in literature. Therefore, this study proposed a new framework to predict the frost depth of soil below the pavement using eight machine learning (ML) algorithms (five single ML algorithms and three ensemble learning algorithms) without geotechnical properties. Among eight ML models, the hyperparameter-tuned gradient boosting model showed the best performance with the coefficient of determination (R²) = 0.919. Furthermore, it was also shown that the developed ML model can be utilized in the prediction of several levels of frost depth and assessing the sensitivity of pavement-related predictors for predicting the frost depth of soils.

Frozen ground regions have recently experienced increasing construction activity due to the vast undeveloped resources they contain. However, frozen soils exhibit thermal and mechanical properties that differ substantially from those of temperate soils, leading to a range of engineering challenges. This study investigates the influence of organic matter content on the freezing behavior of soils through a series of laboratory experiments and machine learning (ML) modeling. Soil samples were collected from Alberta, Canada, and Gangwon Province, South Korea, and their organic matter contents were adjusted using the loss-on-ignition method combined with peat moss addition. Standard Proctor compaction tests and uniaxial compression tests under subzero conditions were performed to evaluate compaction characteristics and strength development. The unfrozen water content was measured at different subzero temperatures to assess thermal and hydraulic responses. The resulting experimental dataset was then used to develop ensemble ML models—random forest (RF) and extreme gradient boosting (XGB)—for predicting unfrozen water content. The results indicate that higher organic matter contents reduce compaction efficiency, increase residual unfrozen water content, and influence strength development under freezing conditions. Both RF and XGB achieved high predictive accuracy, demonstrating their potential as reliable tools for evaluating the freezing behavior of organic soils.