### Advantages and Disadvantages of the Proposed Evaluation Method  
Artificial Neural Networks are progressively used more extensively both in the context of scientific research and industrial applications. Despite the multifaceted benefits their use can bring to the research and industrial processes, careful consideration needs to be given to achieve a balanced assessment of their advantages and drawbacks. The following list outlines some of the main advantages of using ANNs within the context of heat transfer and fire engineering and highlights the benefits of the methodology proposed herein in terms of enhancing these favourable attributes:  
* The use of ANNs can lead to a reduction in the cost and resources associated with further fire and heat transfer testing. This also contributes towards achieving the sustainability targets of research institutions and academic organisations by removing the need for the unnecessary use of fuel, test samples, and the construction of testing facilities. The proposed methodology provides an opportunity for a more efficient and accurate assessment of the ANN's results.
* Artificial Neural Networks can also help reduce the required time for developing and analysing computationally heavy 3D Finite Element heat transfer models. This further reduces the cost associated with acquiring powerful computers to support the aforementioned models. Despite this powerful feature, the use of ANNs needs to be regulated and evaluated; the methodology developed herein sets the foundation for such an evaluation framework.
* ANNs can increase and simplify the reproducibility of heat transfer and fire performance experiments. ANNs introduce efficiency in the exploratory amendment of experiment parameters. Previously recorded figures can feed in and help construct the ANN model, providing the ability to then tweak and replicate the parameters to explore variations of the original experimental arrangements. As this process involves the amendment and adjustment of the input data of ANNs, the workflow introduced in this study can highlight the potential pitfalls arising from this retrospective processing.
* The methodology for input data review proposed herein aims to contribute towards the prevention of misleading or not adequately precise results generated by the ANN, for integration into further research or field applications.
* This body of work also raises awareness regarding the need for a standardised methodology for assessing ANN performance and ANN model development.  
On the other hand, some of the main disadvantages and limitations that need to be addressed before ANNs and the proposed assessment methodology are fully integrated into the scientific and industrial workflow include:  
* The development of ANNs usually requires a large amount of input data, which are not always easy or affordable to obtain. The proposed methodology essentially introduces additional filters and methods for stricter input data quality assessment, which can make the above process even more involved.
* The interpretation of the ANN output is not always straightforward, and its precision can be difficult to judge unless a solid understanding of the expected results has been developed beforehand. The following sections of this study touch on this specific matter and make recommendations.
* To extend the scope of the scientific and industrial application of the proposed model within the context of the heat transfer and fire performance of wall assemblies, further research is needed to incorporate a wider range of material properties, fire loadings, and geometrical wall configurations.  
There are a plethora of proposed model architectures and ML approaches that could potentially enhance the performance of the proposed model. Although the detailed review and integration of such methodologies fall outside the scope of the present study, it would be beneficial for those to be reviewed and compared against the proposed model structure and topology.