## 5 Conclusions  
With the increasing prevalence of frozen soil engineering, there is a growing need for a more profound understanding of the key factor of unfrozen compressive strength in frozen soils. Previous research on frozen soil strength has largely been confined to individual  
Figure 7: Distribution of unfrozen water in different soils: (**a**) Fine-grained soils. (**b**) Coarse-grained soil. soil types, characterized by limited sample sizes and a lack of generalized studies on the strength properties of frozen soil. This study focused on remodeled permafrost as the research subject and employed a machine learning approach to develop a prediction model for unconfined compressive strength using the XGB algorithm. By incorporating the Pearson correlation coefficient, the most influential factors affecting the strength were identified. Additionally, a new dataset was created to investigate the relationship between strength, temperature, and strain rate. In comparison with previous unconfined compressive strength characteristic tests on individual soil samples, this study, leveraging a large dataset, obtained results that were more representative and comprehensive. The following conclusions can be drawn from this study:  
1. The XGB-based prediction model for unconfined compressive strength demonstrated a high level of accuracy in the test set, with a mean squared error (MSE) of 0.39 and a coefficient of determination (\(R^{2}\)) of 0.962. This predictive model contributes to assessing the strength characteristics crucial for engineering design, infrastructure development, and mitigating potential hazards in permafrost regions, thereby aiding in informed decision-making and ensuring the safety and sustainability of various constructions and activities in these environments. 2. The Pearson correlation coefficient was employed to quantify the strength and direction of the relationship between variables. The Pearson correlation coefficient between the temperature and unconfined compressive strength of permafrost was \(-0.590\). For the strain rate and unconfined compressive strength, the Pearson correlation coefficient was 0.370. The analysis indicated a moderate negative relationship between the temperature and permafrost strength and a weak positive relationship between the strain rate and permafrost strength. Utilizing the XGB model provides a more precise representation of such correlations. 3. The unconfined compressive strength of frozen silt is the highest, followed by frozen clay and frozen sand. The reason may be that the content of weak bound water in the fine-grained soil represented by clay and silt was higher, and the weak bound water froze, increasing the cohesion of the soil and obtaining higher strength. Coarse-grained soil, represented by sandy soil, presented a single-grain structure, which weakened the ice cementation ability after freezing by unfrozen water, resulting in a low overall strength. 4. Temperature exerted a substantial influence on the unconfined compressive strength of permafrost. As the temperature decreased, the strength of permafrost exhibited a noticeable upward trend. In the early stages of freezing (\(-2\) to \(-4\)\({}^{\circ}\)C), the strength of the soil experienced a rapid increase due to the freezing of free water within the soil. Subsequently, the growth tendency became relatively stable. In engineering, the adequate strength of frozen soil can be ensured by controlling temperature, thereby mitigating the risk of accidents caused by insufficient strength. 5. The loading rate applied during testing significantly impacted the unconfined compressive strength of frozen soil. A clear increasing trend was observed as the strain rate rose. The sensitivity of the compressive strength to the strain rate was relatively low within the range of \(10^{-5}\)-\(10^{-4}\) s\({}^{-1}\), while it became more pronounced within the range of \(10^{-4}\)-\(10^{-2}\) s\({}^{-1}\).