# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 3 Results and Discussion
### Cast Compressive Strength  
The evaluation metrics for predicting cast strength using different models reveal significant performance differences, as shown in Figure 8. SVM, ANN, and XGBoost models were trained using both BR-augmented and DGAN-augmented data. In terms of R\({}^{2}\) scores, which indicate the proportion of variance explained by the model, all models perform exceptionally well during training, with scores close to 1.0. Among these, XGBoost consistently achieved the highest R\({}^{2}\) scores, with values of 0.999 for DGAN-augmented data and 0.997 for BR-augmented data, outperforming both SVM and ANN.  
During the testing phase, the performance varies more noticeably. For SVM models, those trained with DGAN-augmented data perform the best, indicating that the generative approach of DGAN provided a more robust model for unseen data. In contrast, ANN and XGBoost models show better performance when tested with BR-augmented data, with R\({}^{2}\) scores of 0.980 and 0.982, respectively. This suggests that while DGAN-augmented data helps improve SVM's performance on unseen data, BR-augmented data may still offer some advantages for ANN and XGBoost models during testing despite its limitations in data diversity.  
These performance variations emphasize the advantages and limitations of each data augmentation method. DGAN's generative approach enhances data coverage, leading to more robust models, particularly for SVMs. In contrast, BR-augmented data, which adds Gaussian noise around original points, can improve predictive accuracy for ANN and XGBoost on the current dataset due to its closer alignment with the training data. However, this alignment might not guarantee better performance on new, unseen data.  
The MSE and RMSE metrics, which measure the average squared difference between actual and predicted values, further underscore the superior performance of XGBoost in both the training and testing phases. During training, XGBoost achieves the lowest RMSE, with a value of 1.123 MPa for DGAN-augmented data and 2.155 MPa for BR-augmented data. This demonstrates XGBoost's accuracy and ability to closely match predicted values to actual values.  
However, during testing, all models exhibit higher errors, reflecting the challenge of generalizing to new data. Even so, XGBoost still performs relatively well with BR-augmented data, achieving an MSE score of 27.434 compared to 76.144 for DGAN-augmented data. Similarly, the RMSE values are 8.726 MPa for DGAN and 5.238 MPa for BR, showing that while BR-augmented data helps reduce errors during testing, DGAN's performance, although lower, is still notable given its more comprehensive data generation approach. The MAE, which represents the average absolute differences between predicted and actual values, further highlights XGBoost as the most accurate model during training. It achieves the lowest MAE values of 0.799 MPa for DGAN-augmented data and 1.647 MPa for BR-augmented data. During testing, although the errors increase across all models, XGBoost maintains a performance edge, especially with BR-augmented data, achieving an MAE of 4.731 MPa.  
Overall, XGBoost consistently outperforms SVM and ANN, particularly when trained on BR-augmented data. This consistent performance edge, indicated by lower MSE, RMSE, and MAE values, demonstrates XGBoost robustness and accuracy in both the training and testing phases. Despite the higher errors encountered during testing, XGBoost's ability to maintain relatively lower error metrics with BR-augmented data makes it a reliable choice for prediction tasks, showcasing its effectiveness in utilizing both BR and DGAN augmentation techniques.