# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 3 Results and Discussion
### Slump Flow  
For predicting slump flow, XGBoost demonstrated the highest \(\text{R}^{2}\) values for both training and testing, as shown in Figure 10. Specifically, XGBoost achieved an \(\text{R}^{2}\) of 0.997 for training and 0.794 for testing on DGAN-generated data. With BR-augmented data, XGBoost showed \(\text{R}^{2}\) values of 0.997 for training and 0.979 for testing, indicating excellent predictive accuracy.  
ANN also performed well, particularly with BR-augmented data, achieving a testing \(\text{R}^{2}\) of 0.980. In contrast, SVM trained on DGAN-generated data had the lowest \(\text{R}^{2}\) scores, with 0.969 for training and 0.798 for testing. Regarding MSE values, XGBoost again showed superior performance. For DGAN-augmented data, XGBoost had MSE values of 6.567 for training and 625.82 for testing. With BR-augmented data, the MSE values were 10.698 for training and 60.752 for testing. ANN showed competitive MSE values, particularly with BR-augmented data, having scores of 91.383 for training and 65.066 for testing. SVM trained on DGAN-generated data had the highest MSE value, reaching 611.871.  
The training metrics for both DGAN-trained and BR-trained models are comparable, contrasting with the trend observed for cast and printed compressive strength features, where DGAN-trained models consistently achieved higher training scores. This indicates that BR is an effective augmentation technique for features that span the full range of values, as demonstrated by the slump flow data in Figure 10. Figure 10a,c show that SVM and ANN models trained on DGAN-augmented data performed similarly, with minimal variation between train and test points. This consistency is also observed for SVM and ANN models trained on BR-augmented data, as illustrated in Figure 10b,c. In the case of XGBoost, models trained on BR data exhibit superior performance in both training and testing phases compared to those trained on DGAN data, as shown in Figure 10f.  
RMSE values were also for XGBoost were consistently the lowest. For DGAN-generated data, XGBoost's RMSE was 2.563 mm and 25.017 mm for training and testing, while with BR, the RMSE values were 3.271 mm and 7.794 mm. ANN also performed well, with training and testing scores of 9.599 mm and 8.066 mm for BR-generated data. SVM, however, had a higher RMSE value, 8.842 mm for training and 24.736 mm for testing with DGAN-generated data. When utilizing the DGAN-augmented dataset versus the BR-augmented dataset, XGBoost consistently demonstrated superior predictive accuracy, as indicated by the lowest errors. Specifically, with the DGAN-augmented data, XGBoost achieved a training MAE of 1.839 and a testing MAE of 18.068. In contrast, when using the BR-augmented data, XGBoost's MAE was 2.471 during training and 6.600 during testing. ANN also performed well, particularly with the BR-augmented data, achieving a training MAE of 6.537 and a testing MAE of 6.446. SVM, however, exhibited higher MAE values, especially in the testing phase with the DGAN-augmented data, where it showed an MAE of 17.913.  
Overall, R\({}^{2}\) scores for all models for predicting cast and print compressive strength are comparable, but RMSE, MSE, and MAE scores for the slump are higher than print and cast compressive strength, which are somewhat comparable. The comparable R\({}^{2}\) scores indicate that while the models explain a similar proportion of the variance for all three stated properties, the absolute errors are larger for slump prediction, reflecting the greater challenge in capturing its variability and complex interactions within the model owing to the wide range of values measured for slump flow than the compressive strength.