## 2 Materials and Methods
### Data Presentation
For instance, if the peak load curve (the yellow one) overlaps significantly with the curves of UCS, DC, CT, or (W/G), it implies a strong relationship or dependency between these parameters and the peak load values, verifying the correlation analysis from the Pearson analysis. Also, all parameters are shown in Figure 7. The illustration reveals various subplots, each representing the characteristics of the variables examined in this paper. This might suggest consistent alterations in the composition of the grout mixture as time progresses. For CT, there is a pattern similar to the steps, which means that the required time for curing the grouts increased in distinct periods. The DC plot displays a one-step transition because only two different DC values were in the dataset. The plot in the middle to the left depicts the grout percentage with and without FA contents, showing distinct step changes that suggest a deliberate adjustment in grout amount. This could indicate that the proportion of grout has gradually changed during the research. The graph displaying FA contents exhibits a less uniform trend, with peaks indicating notable fluctuation in the quantity of FA incorporated in the mixture. The UCS plot shows fluctuations and noise, depicting a broad spectrum of UCS values with some variability. In conclusion, the peak load graphs also show fluctuations and unpredictability, indicating differences in measuring the maximum load capacity among various samples/conditions. In general, the illustration shows a variety of behaviours for these parameters, including regular oscillations and complex, volatile trends, demonstrating the dynamic characteristics of the data in this study. Figure 7: Overview of peak load and effective parameters. Regarding the limited dataset of 72 samples, it is crucial to highlight that the sampling process was time-consuming and costly. Each sample was carefully prepared and tested, reflecting the significant effort and resources invested in this research. Despite the small size, the dataset captures a wide range of influential parameters, enabling a thorough examination of the performance factors related to fully grouted rock bolting systems. To validate the model and address potential issues of overfitting or underfitting, we implemented robust validation techniques, including cross-validation and various performance evaluation metrics. Furthermore, the sensitivity analysis conducted in this study enhances our confidence in the model's reliability and generalisation capabilities, affirming our findings' validity despite the constraints of a small dataset. Before modelling, the dataset was randomly divided into training (80%) and testing (20%) sets to ensure unbiased performance evaluation. The training set was used for model training, while the testing set was reserved for performance validation. No resampling techniques, such as cross-validation, were used due to the limited dataset size; however, future studies could consider k-fold cross-validation to improve robustness further. Outliers in the dataset were identified through visual inspection of the violin plots and the IQR method. All variables were normalised to ensure consistency across different feature scales. The normalisation process was done using the min-max scaler to scale the values between 0 and 1, which helped accelerate the convergence of the optimisation algorithms. In developing the baseline XGBoost model, we carefully selected the hyperparameters through manual tuning and grid search to ensure optimal model performance. The learning rate, which controls the contribution of each tree in the model, was set to 0.217. This moderately low value helps prevent overfitting by incremental updates during boosting. The maximum depth of the trees was set to 3, which controls the model's complexity and helps prevent overfitting to the training data. We chose 199 estimators, which balances the trade-off between model accuracy and computational efficiency.