## 4 Results and Discussion
### Performance of Models before Data Augmentation  
Figure 5 show the results of the three models (LightGBM, SVM, and CFNN) before data augmentation. The scatter plots in Figure 5a-c visually depict the models' performance by comparing the predicted and observed values for both the training and test sets. Figure 5a demonstrates that LightGBM provides a moderate fit to the data. Figure 5b, which represents the SVM model, exhibits more scattered points that deviate further from the ideal line, indicating a poorer performance compared to LightGBM. Figure 5c, corresponding to the CFNN model, reveals that the data points are more tightly clustered around the ideal line, suggesting the best fit among the three models.  
The time series plots in Figure 5d-f depict the tendency of the models in the test set. In Figure 5d, corresponding to the LightGBM model, the predicted values show some deviations from the observed values, indicating over- or underestimation tendencies for certain samples. Figure 5e, representing the SVM model, exhibits more pronounced deviations between the predicted and observed values, with noticeable over- or underestimation for some samples. Figure 5f, corresponding to the CFNN model, shows that the predicted values closely follow the observed values, suggesting minimal over- or underestimation tendencies across the test set samples.  
Table 4 provides an overview of the performance of models trained on the original dataset in predicting the compressive strength of geopolymer concrete. Accordingly, the CFNN model exhibited superior performance across all evaluation metrics. It achieved the highest \(R^{2}\) values of 0.902 and 0.842 for the training and test sets, respectively. Additionally, the CFNN model had the lowest error values, with an MAE of 2.973 and 3.940, and an RMSE of 3.348 and 6.442 for the training and test sets, respectively. The CFNN model also demonstrated low percentage errors, with a MAPE of 6.00% for the training set and 18.00% for the test set. Furthermore, the RSR value of 0.411 for the test set suggests a relatively low standardized RMSE compared to the standard deviation of the measured data. The LightGBM model showed a reasonably good fit with \(R^{2}\) values of 0.819 and  
Figure 5: Model performance before data augmentation.  
0.805 for the training and test sets, respectively. However, it exhibited higher error values, with an MAE of 4.357 and 5.593, and an RMSE of 6.287 and 7.24 for the training and test sets, respectively. The MAPE was 17.50% for the training set and 20.10% for the test set, suggesting relatively low percentage errors. The RSR value of 0.600 for the test set indicates a moderate standardized RMSE compared to the standard deviation of the measured data. Similarly, the SVM model displayed signs of overfitting, with a high \(R^{2}\) of 0.893 on the training set but a lower value of 0.783 on the test set. Despite the lower \(R^{2}\) on the test set, the model had lower MAE and RMSE values compared to LightGBM, with an MAE of 3.945 and 4.918, and an RMSE of 5.277 and 7.309 for the training and test sets, respectively. The MAPE was 26.00% for the training set and 22.00% for the test set, indicating low percentage errors. The RSR value of 0.496 for the test set suggests a relatively low standardized RMSE. Overall, the CFNN model demonstrated the most promising performance in predicting the compressive strength of geopolymer concrete among the models evaluated.