Performance Evaluation of Artificial Neural Networks (ANN) Predicting Heat Transfer through Masonry Walls Exposed to Fire  
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Footnote 1: email: {as.s. This study's contribution is twofold; it aims to fill part of the ANN utilisation gap in heat transfer research, on one hand, while stimulating a constructive dialogue regarding the evaluation and significance of input selection for ANNs on the other. The first aim is achieved through the development of an ANN architecture able to make predictions on the thermal performance of masonry wall assemblies exposed to elevated temperatures. It specifically proposes a neural network algorithm capable of predicting the temperature development on the non-exposed face of the wall assemblies over time, when the standard fire curve (as described in Eurocode EN1991-1-2) is applied to the other face of the wall. Although there are a number of studies exploring the use of Machine Learning and Neural Networks in applications such as structural health monitoring [6] and the residual capacity of structural members [7], the adoption of these methods is still not as widespread within the field of fire engineering as in other scientific domains [5]. Equally, there are studies exploring the optimisation of the internal environment for user comfort through the use of Artificial Intelligence [8]; the main focus, though, largely deviates from heat transfer through structural members. The first part of this study's unique contribution is that it combines aspects of fire engineering (exposure to fire and the change of phase of the components of the structural member) with the transient movement of heat through a masonry wall. This work paves the way for a range of practical applications of heat transfer through the composite, non-uniform, and perforated elements of varying material thermal properties with prospects in the field of civil engineering and materials science. The latter is approached with a systematic evaluation of the aforementioned algorithm's performance in terms of accuracy and reliability. Different metrics are used to measure the credibility of the ANN in this specific application and an intuitive comparison to the \"ground truth\" data is also employed to evaluate the final predictions. To ensure a robust model development and evaluation process, a step by step approach is adopted, closely following previously published ANN development protocols as a guideline [9]. The developed evaluation methodology constitutes an initial step towards the establishment of a standardised process of quantifying the input data impact on the ANN's performance. A parallel investigation of a wide range of research facets (other ML techniques, optimisation algorithms, the integration of other scientific fields beyond fire engineering, and heat transfer, to name a few) could provide additional value to the current proposal. Nonetheless, this initial attempt already contributes towards building confidence around the accuracy and reliability of ANN results, and it sets an initial foundation for developing a filter and evaluation process during the selection of input datasets through a quantitative and visual assessment of ANN predictive performance. Several scientific efforts have focused on optimising and assessing the impact of alternative algorithm architectures and the choice of hyperparameters on the performance of ANNs [10]; on the other hand, this study explores the impact of varying degrees of input data quantity and quality. The initially developed ANN architecture is maintained identically throughout the study, while portions of the input data are gradually withheld, with the intention to observe the degradation of the ANN's predictive capabilities. This results in four different regressor models with identical structures but significantly different ultimate performance. The predictions of each model are assessed and the risk of receiving misleading results is commented upon.