# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 3 Results and Discussion  
Three different ML models, specifically SVM, ANN, and XGBoost, were utilized for prediction. The scatter plots in Figures 8-10 illustrate the training and testing outcomes of the predicted features for each model. From Figures 8, 9 and 10a,c,e, it is apparent that the DGAN effectively generates data that addresses the issue of uneven distribution observed in the original data. This is in contrast to Figures 8, 9 and 10b,d,f which demonstrate that the distribution of the BR-augmented data replicates the unevenness present in the real data. BR augments data by introducing Gaussian noise to the real data points. This process results in a concentration of synthetic data around the original data points. As it only introduces slight variations to the existing data without significantly altering its overall distribution, consequently, this leads to replication of the original uneven distribution. The synthetic data generated by BR, therefore, tends to cluster around the existing data points, failing to provide a more comprehensive distribution. Figure 8: Actual vs. predicted cast compressive strength: (**a**) DGAN-SVM, (**b**) BR-SVM, (**c**) DGAN-ANN, (**d**) BR-ANN, (**e**) DGAN-XGBoost, and (**f**) BR-XGBoost. Figure 10: Actual vs. predicted slump flow: (**a**) DGAN-SVM, (**b**) BR-SVM, (**c**) DGAN-ANN, (**d**) BR-ANN, (**e**) DGAN-XGBoost, and (**f**) BR-XGBoost. Figure 9: Actual vs. predicted printed compressive strength: (**a**) DGAN-SVM, (**b**) BR-SVM, (**c**) DGAN-ANN, (**d**) BR-ANN, (**e**) DGAN-XGBoost, and (**f**) BR-XGBoost. On the other hand, DGAN operates differently by being inherently generative. It creates new data points along the entire range of the original data by understanding the underlying feature relationships. This generative approach fills gaps in the distribution, thereby providing more comprehensive and uniform coverage across the whole data range. By generating data points that cover the entire range of the original data, DGAN enhances the representativeness and robustness of the synthetic data. This difference in data augmentation approaches significantly impacts the quality and utility of synthetic data. While BR's method results in synthetic data that mirrors the uneven distribution of the real data, DGAN's approach results in a more evenly distributed synthetic dataset. This even distribution is crucial for training machine learning models as it ensures that the models are exposed to a wide range of scenarios, improving their generalization capabilities. The evaluation metrics for the training and testing of all models are detailed in Table 6. These metrics provide a quantitative assessment of the model's performance. The evaluation parameters reveal a distinct difference in the values of all metrics for models trained on BR-augmented data versus those trained on DGAN-augmented data. During the training phase, performance metrics of all compressive strength features exhibit higher values for models trained on DGAN augmented data in comparison to the same models trained on BR augmented data. Slump flow showed an opposite trend with BR-trained models showing higher values of R\({}^{2}\) than DGAN-trained models. This trend can be attributed to the fact that the initial augmented data for compressive strength features showed large gaps in their distribution, as evident from Figure 7e-h and during augmentation, DGAN reliably filled the gaps in the original data distribution. This gave DGAN-trained models a performance edge during training as the BR-augmented data still had gaps in its distribution, as shown by Figure 8b,d,f and Figure 9b,d,f, while the DGAN-augmented data showed a more uniform data distribution as shown in Figure 8a,c,e and Figure 9a,c,e. In the case of slump flow, the original data did not have any major gaps in its distribution as visible in Figure 7i. The uniform distribution enabled BR to augment the slump flow data equally well as DGAN, as there were no gaps in distribution that DGAN could fill.