# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 3 Results and Discussion
This made the BR-trained models perform marginally better than the DGAN-trained models during the training phase. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{
\begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular} } & \multirow{3}{*}{**Features**} & \multicolumn{6}{c}{**Training**} & \multicolumn{6}{c}{**Testing**} \\ \cline{3-14}  & & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**XGBoost**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**XGBoost**} \\ \cline{3-14}  & & **DGAN** & **BR** & **DGAN** & **BR** & **DGAN** & **BR** & **DGAN** & **BR** & **DGAN** & **BR** \\ \hline \multirow{4}{*}{\(\text{R}^{2}\)} & **Cast** & 0.989 & 0.983 & 0.991 & 0.981 & 0.999 & 0.997 & 0.946 & 0.940 & 0.949 & 0.980 & 0.990 & 0.982 \\  & **Dir.1** & 0.990 & 0.971 & 0.991 & 0.976 & 0.999 & 0.995 & 0.887 & 0.950 & 0.907 & 0.961 & 0.848 & 0.970 \\  & **Dir.2** & 0.991 & 0.981 & 0.992 & 0.980 & 0.999 & 0.996 & 0.934 & 0.946 & 0.938 & 0.934 & 0.910 & 0.972 \\  & **Dir.3** & 0.990 & 0.975 & 0.992 & 0.973 & 0.999 & 0.993 & 0.897 & 0.939 & 0.924 & 0.934 & 0.921 & 0.971 \\  & **Slump flow** & 0.969 & 0.970 & 0.969 & 0.978 & 0.997 & 0.997 & 0.978 & 0.957 & 0.974 & 0.979 & 0.729 & 0.980 \\ \hline \multirow{4}{*}{MSE} & **Cast** & 13.976 & 24.950 & 11.686 & 28.081 & 1.261 & 4.643 & 80.394 & 89.575 & 76.144 & 29.143 & 148.779 & 27.434 \\  & **Dir.1** & 10.266 & 31.892 & 9.553 & 27.089 & 0.917 & 5.409 & 125.541 & 56.019 & 102.990 & 33.276 \\  & **Dir.2** & 8.805 & 21.914 & 7.889 & 22.815 & 0.893 & 4.571 & 78.353 & 64.098 & 73.786 & 72.02 & 108.577 & 33.969 \\  & **Dir.3** & 7.866 & 19.182 & 6.452 & 20.540 & 0.732 & 5.582 & 79.760 & 47.489 & 58.614 & 51.067 & 61.272 & 22.629 \\  & **Slump flow** & 78.175 & 91.383 & 76.782 & 66.545 & 6.567 & 10.698 & 611.871 & 131.040 & 625.829 & 65.066 & 823.615 & 60.752 \\ \hline \multirow{4}{*}{RMSE} & **Cast (MPa)** & 3.738 & 4.995 & 3.418 & 5.299 & 1.123 & 2.155 & 8.966 & 9.464 & 8.726 & 5.398 & 12.198 & 5.238 \\  & **Dir.1** (MPa)** & 3.204 & 5.647 & 3.091 & 5.205 & 0.958 & 2.326 & 11.205 & 7.485 & 10.148 & 6.584 & 13.011 & 5.769 \\  & **Dir.2** (MPa)** & 2.967 & 4.681 & 2.809 & 4.776 & 0.945 & 2.207 & 8.852 & 8.006 & 8.590 & 8.873 & 10.337 & 5.828 \\  & **Dir.3** (MPa)** & 2.805 & 4.380 & 2.540 & 4.532 & 0.856 & 2.363 & 8.931 & 6.891 & 7.656 & 7.146 & 7.288 & 4.757 \\  & **Slump flow** & 8.842 & 9.559 & 8.763 & 8.157 & 2.563 & 3.271 & 24.736 & 11.447 & 25.017 & 8.066 & 28.699 & 7.794 \\  & **(mm)** & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{**MAE**} & **Cast (MPa)** & 2.383 & 3.460 & 2.532 & 4.158 & 0.799 & 1.647 & 6.691 & 6.182 & 6.550 & 3.761 & 8.039 & 4.731 \\  & **Dir.1** (MPa)** & 2.114 & 3.729 & 2.334 & 4.014 & 0.686 & 1.758 & 8.272 & 5.794 & 7.941 & 5.567 & 9.436 & 5.118 \\  & **Dir.2** (MPa)** & 1.919 & 3.310 & 2.066 & 3.711 & 0.676 & 1.682 & 6.322 & 6.547 & 6.328 & 7.654 & 7.725 & 5.420 \\  & **Dir.3** (MPa)** & 1.787 & 3.193 & 1.876 & 3.577 & 0.606 & 1.804 & 6.523 & 6.144 & 5.408 & 6.620 & 5.806 & 4.309 \\  & **Slump flow** & 6.153 & 6.537 & 6.655 & 6.306 & 1.839 & 2.471 & 17.913 & 9.280 & 18.068 & 6.446 & 21.350 & 6.600 \\  & **(mm)** & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics score for training and testing of all predictive models. The feature-wise testing metrics for all models are shown in Figure 11. The evaluation parameters from the testing phase show that models trained on BR-augmented data surpass those trained on DGAN-augmented data. However, statistical analysis indicates that DGAN-generated data more accurately represents the real data and effectively addresses gaps in its distribution. This should logically lead to better performance for the models trained with DGAN data. The observed performance difference can be attributed to the nature of the BR-augmented data, which is closely aligned with the original data points.