## 5 Conclusions and Further Research  
This paper contributes towards the further integration of ANNs in the field of heat transfer through building materials and assemblies. Arguably, there is a wide scope for further development of this research in directions such as the use of different algorithm types and structures, an extended range of building assemblies and materials, and different thermal loadings. Nevertheless, some first conclusions can be drawn from this initial effort, informing future scientific work aiming to employ Artificial Neural Networks for the description of heat transfer phenomena. Although good metric results quantifying the replicative validity of the algorithm offer an indication of a functional ANN, they do not necessarily constitute evidence of a fully operational and reliable network. As such, the use of further validation techniques is of paramount importance. A comparison against unknown data (methodology followed in this study) is an objective way of ensuring the constructed model behaves and performs as expected. When \"external\" data is not available or difficult to obtain, k-fold cross validation could provide a route for building some confidence in the performance of the model. To mitigate the risk of overfitting, neuron \"dropout\" can be introduced as part of the algorithm's hyperparameters. This would artificially weaken the fitting of each neuron to the supplied training data. As seen in the previous paragraphs, metrics and indices can sometimes be misleading. An intuitive understanding of the predictions and their relevance to factual data is necessary to mitigate the impact of the \"black box\" phenomenon of blindly accepting output generated by an AI model. Outlining the expectations of the models' output in advance could provide a measure against which a preliminary assessment of the ANN's predictions can be undertaken. In the same direction, producing visual representations of the output at every stage can enhance the understanding of the relationship between the ground truth and the predictions and can imminently highlight subtle inaccuracies or major errors. Depending on the nature and contribution of each parameter to the physical phenomenon, the impact of its loss from the training data can have a more severe impact on the predictive capability of the Neural Network. It is worth researching this relationship by quantifying the contribution of various parameters on the development of a physical phenomenon and then training ANNs with a gradual deprivation of the variables in question. This could provide an opportunity for quantifying the correlation of various physical parameters with the performance of the artificial neural model. The ANN's development, analysis, and interrogation need to be considered within the wider context of a specific scientific study or practical application. This contextualisation can have a significant effect on the specification of required accuracy levels for the chosen evaluation methodology. Different scientific applications have different required levels of accuracy in their produced results. As such, it is prudent to avoid trying to establish ill-defined accuracy thresholds without specific application requirements to hand. This study presented the underpinning principles of an assessment methodology for the evaluation of ANN predictions and highlighted potential pitfalls arising from the use of ANNs within a masonry wall heat transfer context. The focus of the present work is to identify the impact of varying quantities and qualities of input data on the accuracy of a specific ANN architecture and to propose a methodology for demonstrating this relationship. This objective has been accomplished by presenting the inferior results generated by the models trained with reduced amounts of data.