## 4 Results and Discussion
### Discussion and Limitations  
The comparison of model performance before and after data augmentation is visualized in Figure 8. The findings align with relevant studies that have utilized synthetic data for ML models in predicting the compressive strength of concrete [11; 13]. Research conducted by [PERSON] et al. (2022) [11] highlights the effectiveness of integrating synthetic data, showcasing a significant improvement in the accuracy of deep learning models (i.e., CNNs), compared to traditional ML models. Similarly, this study underscores the efficacy of data augmentation methods in enhancing model predictive capabilities, particularly for CFNNs, despite the inherent variability in precursor chemicals. Additionally, the results suggest that the implementation of data augmentation techniques for SVMs leads to more substantial improvements compared to tree-based models, although the distinction may not be immediately apparent. While the use of synthetic data has proven effective in enhancing the performance of the LightGBM and CFNN models, as evidenced by the improvements in metrics such as MAE, RMSE, RSR, and WMAPE, its impact on the MAPE of the SVM model remains limited. This can be attributed to the inherent sensitivity of SVM models to the presence of outliers in the dataset [63]. Consequently, the outliers observed in the coarse aggregate and fine aggregate features may have adversely influenced the SVM model's performance relative to the LightGBM and CFNN models. Notwithstanding the observed enhancements in model performance, it is crucial to acknowledge that the generation of synthetic data in this study does not necessarily augment the generalizability of the models. As demonstrated in Table 5, the minimum, maximum, and skewness values of the synthetic data are either reduced or maintained at the same level as the original dataset. However, it is worth noting that generating data with broader coverage may potentially introduce model uncertainty due to extrapolation, thus necessitating further investigation to assess the trade-offs between model performance and generalizability. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Model** & **Dataset** & **R\({}^{2}\)** & **MAE** & **RMSE** & **MAPE** & **RSR** & **WMAPE** \\ \hline LightGBM & Train & 0.913 & 3.004 & 4.676 & 10.00\% & 0.282 & 7.679\% \\  & Test & 0.877 & 3.366 & 4.657 & 10.32\% & 0.413 & 9.413\% \\ \hline SVM & Train & 0.922 & 2.373 & 4.256 & 12.00\% & 0.281 & 4.323\% \\  & Test & 0.863 & 2.897 & 4.61 & 21.00\% & 0.399 & 5.757\% \\ \hline CFNN & Train & 0.956 & 1.037 & 2.982 & 18.10\% & 0.181 & 2.717\% \\  & Test & 0.942 & 1.546 & 3.836 & 22.83\% & 0.194 & 3.265\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model performance. Figure 8: Comparison of R\({}^{2}\) and error indices of the models before and after data augmentation. Despite the high performance in the test set, it should be noted that the test set and training set in this study are constructed using the same dataset obtained from open literature. This can cause overoptimistic performance estimates because the data distribution in both sets is identical. Additionally, it is important to consider that data sourced from previous studies may not always be reliable and the presence of measurement biases among multiple studies further adds to the uncertainty of prediction outcomes. Moreover, the data collected in this study is confined to specific countries and regions, notably China, India, Iran, and Australia, where geopolymer research has received considerable attention. Consequently, the dataset lacks comprehensive international representation, thereby limiting its scope and exhaustiveness. To interpret the developed model effectively, permutation feature importance and SHapley Additive exPlanations (SHAP) were adopted, shedding light on the intricate interplay between input features and their influence on compressive strength. These two interpretability techniques were conducted within CFNNs, recognized as the most promising model.