## 2 Modelling and Methods
### ANN Development Protocol
#### 2.5.6 Model Validation  
Once the ANN is constructed, trained, and functional, it is necessary to evaluate its performance before deploying it in field operations or using it for further scientific research. The evaluation process of the ANN constitutes the focal point of this study, and although it will be explained thoroughly in the results section, it is worth briefly mentioning the three core evaluation aspects, along with the various metrics to be considered. An outline of the implementation method of the above, within the context of this research effort, will also be given, with the intention of gradually introducing the structure of the upcoming results section.  
There are three main steps in validating the functionality and reliability of an artificial neural network [9]. The \"replicative validity\" is the first thing to check to ensure that the ANN is functional and captures the underlying mechanisms of the physical phenomenon. Essentially, the algorithm needs to be able to replicate the known data observations that were offered as input (including the training and testing sets). This process yields \"obvious\" results, but it does also provide a sanity check that the algorithm has captured at least the minimum relationship between the independent and dependent variables. The use of fitness metrics or visual observation of comparative graphs between predictions and provided data can aid in this direction. In this study, both methods, metrics and visual inspection of the algorithm's ability to replicate the data, have been employed.  
The validation of the predictive capacity of the algorithm is the second stage in building confidence before its implementation in real applications. In this step, the ability of the algorithm to make accurate predictions, when provided with unknown (not included in the original training data set) input observations, is assessed by the use of specific efficiency indices. The impact of training some models with progressively fewer input data becomes apparent at this stage. An observation of the diminishing scores of the various metrics and also the deviation of the graphical representation of the predictions from the ground truth values elucidates the major contribution that appropriately rich and diverse input data can have on the development of an effective ANN.  
Finally, the \"structural validity\" of the model needs to be confirmed. As part of this step, the neural network is checked against \"a priori knowledge\" of how the physical system under consideration works [9]. Apart from making correct predictions on specific values, the ANN needs to prove a holistic understanding of the underlying mechanisms that define the phenomenon that is being studied. In this study, instead of generating only individual predictions, the ANN is requested to predict the whole time series of the phenomenon. Thus, the structural validity of the ANN is evaluated through a comparison of the predicted physical behaviour against the known development of the heat transfer process through the various wall samples.  
In the previous paragraphs, reference was made to the metrics used to assess the performance of the ANN in terms of accuracy and reliability. Table 7 presents the main indices used as part of this work [41].