## 5 Conclusions and Future Works  
This study examines the impact of using synthetic data on ML models that predict the compressive strength of geopolymer concrete. For this purpose, 930 data points were collected from the open literature. The TGAN method was employed to generate geopolymer mixture and compressive strength data. The ML model consists of LightGBM, SVM, and CFNN algorithms, representing tree-based, support vector, and neural network algorithms, respectively. These models were trained using the generated data and subsequently evaluated using the real dataset. A comparison was made with the corresponding model using the original dataset. Based on the obtained results, the following conclusions can be drawn:  
* TGAN proves capable of generating mixture and compressive strength data for geopolymer concrete. The synthetic data significantly improves the performance of ML models, as evident from the increased R\({}^{2}\) values and reduced error indices such  
Figure 10: Schematic diagram of model deployment and improvement process. Figure 9: The importance of each feature of CFNN model. (a) Permutation importance. (b) SHAP value. as MAE, RMSE, MAPE, RSR, and WMAPE. The CFNN model exhibited the most improvement, followed by LightGBM and SVM. * The enhanced performance of the models indicates that the heterogeneity in the type or quality of precursors in the collected data does not significantly affect the data generation ability and performance of ML models. * Due to the presence of numerous outliers and skewed characteristics of the data, the SVM model was greatly impacted in both the original and synthetic datasets. * The generated data statistics demonstrate that the characteristics of the data are quite similar to the original data. This indicates that the TGAN model generated reliable data. However, it is important to note that using such data merely improves accuracy without enhancing the generalization capabilities of the models. * In addition, this study demonstrates limitations concerning the generalizability of the model, the scope of inputs, the absence of validation with actual experimental data, and regional bias within the dataset. Several avenues for future research can be pursued to address the limitations identified in this study. Firstly, prioritizing exploration of generator architectures within GANs or employing of bounded GANs could enable better control over the range of generated data. Furthermore, the generalization capability of the models can be enhanced through the implementation of regularization techniques (e.g., dropout, and L1 and L2 regularization), cross-validation, and the adaptation of open-source algorithms tailored to the characteristics of the data. Instead of solely relying on integration algorithms, future research should focus on enhancing existing algorithms to better align with the characteristics of data. To assess practical effectiveness, the developed TGAN and predictive models necessitate further verification through experimental data or real-world project implementation. Moreover, expanding the scope of input variables, such as the chemical and physical composition of precursors, curing duration, and methodologies, could potentially improve the predictive accuracy of the models. Expanding the dataset to include geopolymer research across diverse regions and countries also serves to enhance the internationality and generalizability of the model. The aggregation of a comprehensive dataset holds promise for broadening the scope of output variables to encompass additional mechanical properties, including tensile strength, flexural strength, durability, and the effects of aging on geopolymer concrete. Conceptualization, H.A.T.N. and D.H.P. ; Data curation, H.A.T.N. ; Writing--original draft, H.A.T.N. ; Writing--review & editing, D.H.P. ; Visualization, H.A.T.N. ; Supervision, Y.A. All authors have read and agreed to the published version of the manuscript.