## 2 Data Preprocessing  
### Data Preprocessing  
In this investigation, a comprehensive database was constructed by compiling soil physical properties and unconfined compressive strength test data from the relevant literature sources [12; 17; 23; 32; 33; 34; 35]. These data are not randomly generated but rather obtained from rigorous experimental processes, ensuring the accuracy and reliability of the information. During the data collection process, with the unconfined compressive strength of frozen soil as the core physical indicator, the literature related to temperature variations and strain rates was gathered, and the raw data along with environmental information were documented. Utilizing these data for further analysis can lead to more reliable results. The collected data were utilized to develop a strength prediction model using the XGB algorithm.  
If the extraction of data from images using Origin software (2018C SR1 Version) is required, it is crucial to select clearly discernible data points to minimize potential errors during the data extraction process. The key factors taken into account for the strength prediction model development based on the XGB algorithm included the grain size distribution, dry density, initial moisture content, temperature, and strain loading rate.  
To ensure a high prediction accuracy and generalization ability of the XGB prediction model, preprocessing of the parameters was necessary. A total of 487 datasets were compiled from the results of unconfined compressive strength tests reported in the literature [12; 17; 18; 23; 32; 33; 34; 35; 36; 37; 38; 39]. Since the XGB algorithm utilizes gradient descent to search for the optimal solution, it can face difficulties in convergence when there is a significant difference in magnitude among different input variables. To address this issue, the data were standardized. Normalization helps eliminate scale differences between features, ensuring that the model's impact on each feature is relatively balanced and facilitating faster convergence of the algorithm [40]. Standardization transformed the data into a normal distribution with a mean of 0 and a standard deviation of 1, as represented by Equation (1).  
The purpose of dividing the dataset into training and testing sets was to assess the model's performance on unseen data. The training set was utilized for training the model parameters, while the testing set was employed to evaluate the model's ability to generalize to new data, i.e., whether the model could effectively adapt to unseen samples. The choice of the partition ratio involved a trade-off. If the training set was too small, the model may not learn sufficiently complex patterns, leading to underfitting. Conversely, if the testing set was too small, the evaluation results may lack reliability. In common practice, a widely accepted and used partition ratio for machine learning is 70% for training and 30% for testing. Therefore, this widely agreed-upon ratio was adopted in this paper as well. A random selection of 70% of the samples (341 groups) was used for training, while 30% (146 groups) were used for testing. The overall distribution of the data is illustrated in Table 1.  
\[x\prime=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(x\) is the sample value, \(\mu\) is the sample mean, and \(\sigma\) is the sample standard deviation.