## 4 Results and Discussion
### Discussion and Limitations
Permutation feature importance evaluates the variation in prediction error resulting from the permutation of feature values. If prediction error remains unchanged after shuffling values, the feature is considered 'unimportant,' signifying the model's disregard for it in prediction. This approach offers a concise, comprehensive understanding of the model's behavior. SHAP, grounded in coalitional game theory, conceptualize prediction as a game, with features akin to players. The SHAP value assigned to each feature delineates its average marginal contribution across all conceivable coalitions, thereby elucidating its impact on attaining a heightened or diminished final prediction outcome. Figure 9 presents the overall importance of the features. The most influential features for predicting compressive strength are the specimen age and curing temperature, with permutation importance of 0.38 and 0.35, respectively. Similar results were observed in the SHAP analysis, with slight discrepancies in the influence of slag and fine aggregate content between the two analyses. SHAP analysis presents that higher slag content generally results in increased compressive strength, denoted by the prominent red coloring on the right side of the slag feature. Conversely, higher fly ash content tends to have a detrimental effect on compressive strength, as evidenced by the pink coloring on the right side of the fly ash feature. The impact of fine aggregate content is more nuanced, with both positive and negative effects depending on the specific value of the feature. Other features, such as sodium hydroxide, water content, coarse aggregate, and sodium silicate, exhibit relatively smaller impacts on compressive strength prediction, as indicated by their narrower ranges of SHAP values and lower permutation importance. Superplasticizer content demonstrates a significantly smaller influence compared to the other features (permutation importance = 0.02). It can be observed that that variables of specimen age and curing temperature are the most important features in the decision making of the CFNN model. These factors are expressed in integers and have a high frequency of repetition according to the curing regime. Therefore, the TGAN model can capture features and generate better data. Meanwhile, factors with many outliers such as superplasticizer and coarse aggregate are the least influential features. These may be the reasons why the CFNN model benefits a lot from synthetic data. Additionally, a study conducted by [PERSON] et al. (2022) [64] highlights the impact of curing time and curing method (i.e., oven, steam) on the prediction of ML models. However, these factors were not considered in the current study due to the problem of high dimensionality associated with the complexity of the dataset. Incorporating these factors with larger datasets can improve the accuracy of the prediction model. Considering model characteristics and limitations, a process for deploying and enhancing models was proposed in Figure 10. The developed TGAN and predictive models can be refined using real data from users labeled with the prediction results. The effectiveness of using the results of the ML model to further improve the model is demonstrated in the research of [PERSON] et al. (2022) [61]. This ongoing learning process ensures that the deployedmodels can enhance generalization and mitigate temporal and measurement uncertainties, ultimately improving their performance and reliability over time.