# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 3 Results and Discussion
### Anisotropic Compressive Strength  
The performance of three machine learning models--SVM, ANN, and XGBoost--using two different augmented datasets (DGAN and BR) for predicting the anisotropic compressive strengths of printed concrete (Dir.1, Dir.2, Dir.3) is illustrated in Figure 9. For Dir.1, XGBoost consistently outperformed the other models, achieving the highest R\({}^{2}\) values. With DGAN-augmented data, XGBoost attained a training R\({}^{2}\) of 0.999 and a testing\(\text{R}^{2}\) of 0.887. It scored 0.995 for training and 0.950 for testing for BR-augmented data. ANN and SVM demonstrated slightly lower performance compared to XGBoost. In Dir.2 and Dir.3, similar trends were observed, with XGBoost again achieving the highest \(\text{R}^{2}\) values, followed closely by ANN and SVM. Notably, for Dir.2 using BR-augmented data, XGBoost achieved a testing \(\text{R}^{2}\) of 0.972, while for Dir.3, it reached 0.971. These results highlight XGBoost's robustness and ability to generalize well across different data augmentations and directions.  
XGBoost consistently achieved the lowest MSE across all directions, further demonstrating its accuracy. For Dir.1, XGBoost had an MSE of 0.917 during training and 169.290 during testing with DGAN-augmented data, while with BR-augmented data, the MSE was 5.409 for training and 33.276 for testing. ANN and SVM exhibited higher MSE values, indicating less accurate predictions. In Dir.2, XGBoost maintained low MSE values, with scores of 0.893 and 106.857 for training and testing with DGAN-augmented data and 4.871 and 33.969 for training and testing with BR-augmented data. A similar trend was observed in Dir.3, where XGBoost again showed the lowest MSE value for testing, achieving 22.629 with BR-augmented data. XGBoost also demonstrated superior performance in MAE, indicating fewer errors in predictions. For Dir.1, XGBoost achieved MAE values of 0.686 MPa for training and 9.436 MPa for testing with DGAN-augmented data, while with BR-augmented data, it scored 1.758 MPa for training and 5.118 MPa for testing. In Dir.2, XGBoost had MAE scores of 0.676 MPa for training and 7.725 MPa for testing with DGAN-augmented data. For Dir.3, XGBoost recorded MAE values of 0.606 MPa for training and 5.806 MPa for testing, consistently outperforming ANN and SVM. In summary, across Dir.1, Dir.2, and Dir.3, XGBoost consistently demonstrated superior performance compared to SVM and ANN in all metrics, particularly when using the BR dataset for training.  
The testing performance metrics tests reveal a consistent trend, with BR-trained models outperforming DGAN-trained models. Among the three orthogonal directions, BR-trained models exhibit similar performance levels across the board, except for the BR-trained ANN model, where Dir.1 significantly outperforms Dir.2 and Dir.3. Conversely, for the DGAN-trained SVM and ANN models, Dir.2 achieves the highest \(\text{R}^{2}\) score, while Dir.3 leads in the DGAN-trained XGBoost model, followed by Dir.2. Despite the similar training scores, the variability and lack of a discernible trend in the testing results among the three directions suggest that anisotropic compressive strength is a complex phenomenon. To enable a single model to reliably predict anisotropic compressive strength, an increase in the number of features is necessary. This complexity arises primarily because anisotropic compressive strength is heavily influenced by interlayer bonding, which depends on factors such as layer thickness, layer open time, and extrusion properties, including extrusion rate and shape.  
XGBoost consistently outperformed the other models, achieving the highest \(\text{R}^{2}\) values and lowest MSE, especially with BR-augmented data. In Dir.1, XGBoost led in both the training and testing phases, followed by ANN and SVM. This trend continued for Dir.2 and Dir.3, with XGBoost demonstrating superior generalization, accuracy, and robustness.