## 4 Discussion  
Although an algorithm capable of predicting the temperature developed on the wall samples' non-exposed face was eventually constructed, a few items worth highlighting and discussing further appeared during the development and evaluation process. These are listed in the following paragraphs, with the intention to evoke thoughts and discussion regarding potential pitfalls and respective solutions when the ANNs are employed on heat transfer through masonry wall applications. The perfect fit of ANN 1 with the ground truth is not a surprising result. The network was trained with the full training data, which means that its comparison with the ground truth merely verifies whether its predictions can replicate already-known patterns and figures. Despite its obvious results, however, this step is far from unnecessary, as it ensures the replicative validity of the developed algorithm. It helps in building confidence that the network is not only functional but that it is also able to identify and capture the underlying patterns in the provided dataset. After achieving this first level of validation, the research could proceed by exploring the limits of the algorithm architecture by varying the quality and quantity of the provided training data. ANN 2, deprived of mid-range insulation thickness values, and ANN 3, deprived of mid-range emissivity coefficient values, both had difficulty in achieving equally high predictive rates as ANN 1, which was trained with the full range of data. It is worth highlighting that the predictions of ANN 2 lay closer to the ground truth but demonstrated some local irregularities. On the contrary, the predictions generated by ANN 3 lay further from the ground truth curve (as reflected by the more onerous performance indices); however, these were largely offset from the observed figures, following their smooth curvature. This raises questions as to the impact different variables may have on the performance of ANNs, depending on their contribution to the physical phenomenon under consideration. The emissivity coefficient is a constant value throughout the finite element analysis, while properties relating to the EPS are time-dependent (within the context of this study). The physical importance of the independent variables needs to be well understood before they are incorporated into an ANN structure. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test Sample** & **AME** & **MAE** & **RAE** & **PDIFF** & **PEP** & **RMSE** & **R2** \\ \hline TS1 & 3.83 & 0.90 & 1.89\% & \(-\)0.44 & \(-\)0.28\% & 1.20 & 0.9995 \\ TS 2 & 4.26 & 1.93 & 3.72\% & 3.72 & 2.03\% & 2.48 & 0.9981 \\ TS 3 & 6.06 & 2.08 & 6.37\% & 4.42 & 3.30\% & 2.73 & 0.9946 \\ TS 4 & 11.42 & 3.80 & 26.50\% & 11.42 & 15.04\% & 5.36 & 0.8990 \\ TS 5 & 12.43 & 4.16 & 10.32\% & \(-\)4.02 & \(-\)2.70\% & 5.81 & 0.9832 \\ TS 6 & 4.13 & 1.92 & 4.64\% & 4.03 & 2.66\% & 2.25 & 0.9976 \\ Average & 7.02 & 2.47 & 8.91\% & 3.19 & 3.3\% & 3.31 & 0.9787 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Metric scores for ANN 1, as the dominant network, on unknown data sets (wall test samples). The most data-deprived network, ANN 4, presents the most irregular graphic form of prediction results. The comparison between the performance indices for ANN 4 and ANN 3 show that the former performed better. However, a visual observation shows clearly that the prediction line of ANN 4 is more erratic compared to ANN 3, whose line is just \"offset\" (i.e., consistently overestimating or underestimating compared to the ground truth). Although networks ANN 2 and ANN 3 failed to make accurate predictions (at least to the degree ANN 1 did), they appeared to capture the underlying principles and function of the physical phenomenon. On the other hand, the lack of resemblance to the ground truth curve demonstrates that the capture of the underlying mechanisms of the physical phenomenon by ANN 4 is poor, despite its better metrics. At the training stage, all algorithms returned high indices of fitness to the test data.