Seasonally frozen soils are exposed to freeze-thaw cycles every year, leading to mechanical
property deterioration. To reasonably describe the deterioration of soil under different
conditions, machine learning (ML) technology is used to establish a prediction model
for soil static strength. Six key influencing factors (moisture content, compaction
degree, confining pressure, freezing temperature, number of freeze-thaw cycles and
thawing duration) are included in the modelling database. The accuracy of three typical
ML algorithms (support vector machine (SVM), random forest (RF) and artificial neural
network (ANN)) is compared. The results show that the ANN outperforms the SVM and
RF. Principal component analysis (PCA) is combined with the ANN, and the PCA-ANN algorithm
is proposed, which further improves the prediction accuracy. The deterioration of
soil static strength is systematically researched using the PCA-ANN algorithm. The
results show that the soil static strength decreased considerably after the first
several freeze-thaw cycles before the strength plateau occurred, and the strength
reduction increased significantly with increasing moisture content and compaction
degree. The PCA-ANN model can generate a reasonable prediction for the static strength
or other soil properties of seasonally frozen soil, which will provide a scientific
reference for practical engineering.