## 3 Results
### Performance of the Dominant ANN Model  
Following the review of the results presented in the previous paragraphs, the superiority of ANN 1 became apparent. To assess the \"dominant\" model's performance against completely unknown data, 6 more tests were carried out. Figure 8 is the visual representation of the results of these additional tests. The goodness of fit between the ground truth (data obtained from finite element model analysis) and the predictions made  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Wall Assembly** & **AME** & **MAE** & **RAE** & **PDIFF** & **PEP** & **RMSE** & **R2** \\ \hline WA1 & 28.46 & 10.87 & 45.59\% & 22.58 & 20.95\% & 15.05 & 0.7023 \\ WA2 & 45.00 & 16.21 & 57.80\% & 45.00 & 39.93\% & 22.55 & 0.4870 \\ WA3 & 41.09 & 9.64 & 40.57\% & 27.81 & 24.18\% & 16.01 & 0.7081 \\ WA4 & 48.84 & 15.12 & 47.49\% & 27.71 & 24.25\% & 22.35 & 0.5922 \\ WA5 & 28.77 & 11.37 & 35.85\% & 14.56 & 11.07\% & 15.06 & 0.8271 \\ WA6 & 30.34 & 14.99 & 46.29\% & 24.19 & 19.55\% & 19.18 & 0.7171 \\ Average & 37.08 & 13.03 & 45.60\% & 26.98 & 23.3\% & 18.37 & 0.6723 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Metric scores for ANN 3 in the testing phase.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Neural Network** & **Loss** \\ \hline ANN 1 & 5.0840 \(\times\) 10\({}^{-4}\) \\ ANN 2 & 5.0721 \(\times\) 10\({}^{-4}\) \\ ANN 3 & 2.1836 \(\times\) 10\({}^{-4}\) \\ ANN 4 & 2.4811 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Final metric scores for each ANN following the completion of their training with their respective training dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Wall Assembly** & **AME** & **MAE** & **RAE** & **PDIFF** & **PEP** & **RMSE** & **R2** \\ \hline WA1 & 18.13 & 8.36 & 35.08\% & 18.13 & 16.82\% & 10.18 & 0.8639 \\ WA2 & 22.03 & 10.46 & 37.30\% & 14.55 & 12.92\% & 12.39 & 0.8451 \\ WA3 & 29.39 & 9.82 & 41.35\% & 28.83 & 25.06\% & 13.25 & 0.8002 \\ WA4 & 33.29 & 15.44 & 48.49\% & 29.33 & 25.66\% & 19.49 & 0.6898 \\ WA5 & 31.66 & 8.25 & 26.01\% & 31.65 & 24.06\% & 12.56 & 0.8797 \\ WA6 & 22.70 & 7.91 & 24.42\% & 22.70 & 18.34\% & 10.58 & 0.9140 \\ Average & 26.20 & 10.04 & 35.44\% & 24.20 & 20.5\% & 13.07 & 0.8321 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Metric scores for ANN 4 in the testing phase.  
by the ANN is easy to observe. This is further founded and reinforced by the metrics included in Table 13.  
Figure 8: Graphical representation of the predictive performance of the dominant ANN model against completely unknown wall assembly combinations.  
The ANN manages to predict the ultimate temperature developed on the non-exposed surface of the wall test samples with a relatively low error, 3.3% on average. Although on one occasion (Test Sample 4) the prediction of peak temperature differs from the observed value by 11.42 \({}^{\circ}\)C (15.04% deviation from the actual temperature), on average, the predictions lie within 3.19 \({}^{\circ}\)C of the ground truth. This high predictive performance is observed not only on the peak temperature, as an isolated success, but also by the overall low mean absolute error that ranges from 0.90 \({}^{\circ}\)C to 4.16 \({}^{\circ}\)C.  
Tests TS4 and TS5 appear to have more onerous metric results. The absolute maximum errors are 11.42 \({}^{\circ}\)C (in peak temperature, as explained above) and 12.43 \({}^{\circ}\)C, respectively. These can be observed in the graphic representation of the results as deviations of the prediction curve from the ground truth curve. Despite these local inconsistencies with the observed figures, the overall high coefficient of determination (0.9787 on average between tests) and low root mean squared error (3.31 on average) indicate a high-performing model.