Examination of Determinants and Predictive Modeling of Artificially Frozen Soil Strength Utilizing the XGBoost Algorithm

Abstract

A freezing method is usually employed in the construction of metro links. Unconfined compressive strength (UCS) is a pivotal mechanical parameter in freezing design. Due to the limitations of indoor experiments and the complexity of influencing factors, the applicability of empirical strength formulas is poor. This study predicts the strength of frozen soil with different particle size distributions based on the highly integrated XGBoost algorithm. Compared with other empirical formula methods, the accuracy is high. Through the analysis of Pearson’s correlation coefficient results, further analysis is needed on the nonlinear correlation between the temperature, the strain rate, and the unconfined compressive strength of frozen soil. The results indicated a strong negative correlation between temperature and unconfined compressive strength; the strength initially increased at a faster rate, slowed down during the intermediate phase, and again increased at a faster rate toward the end. There was a positive correlation between the strain rate and the unconfined compressive strength, with the strength exhibiting varying sensitivities to different sizes of strain rates. When the strain rate was relatively small, the strength increased slightly; as the strain rate increased, the strength increased more significantly. Different soils showed similar trends, but differences in the particle size distribution resulted in variations in the final strength. This study can provide a scientific basis for predicting the strength of soil bodies in the freeze–thaw construction of subway connection tunnels.