The multiple benefits Artificial Neural Networks (ANNs) bring in terms of time expediency and reduction in required resources establish them as an extremely useful tool for engineering researchers and field practitioners. However, the blind acceptance of their predicted results needs to be avoided, and a thorough review and assessment of the output are necessary prior to adopting them in further research or field operations. This study explores the use of ANNs on a heat transfer application. It features masonry wall assemblies exposed to elevated temperatures on one side, as generated by the standard fire curve proposed by Eurocode EN1991-1-2. A juxtaposition with previously published ANN development processes and protocols is attempted, while the end results of the developed algorithms are evaluated in terms of accuracy and reliability. The significance of the careful consideration of the density and quality of input data offered to the model, in conjunction with an appropriate algorithm architecture, is highlighted. The risk of misleading metric results is also brought to attention, while useful steps for mitigating such risks are discussed. Finally, proposals for the further integration of ANNs in heat transfer research and applications are made.

Many of the emblematic buildings of historical importance that have been constructed throughout human history still survive today. However, a significant number has been destroyed by fire. Despite the impact this loss has on cultural heritage, important lessons can also be learnt, enhancing our understanding on how fires develop in historical structures and why they occur in the first place. A review of the existing fire design approaches, in conjunction with the heritage building values and fundamental conservation principles, initiates a dialogue in terms of acceptable interventions and fire protection solutions. The aim of this study is to provide contemporary scientists, conservation professionals and building owners with an insight of how building physics affect the fire performance of historic structures, highlight common risks following a thorough literature review and discuss the role of the fire and conservation engineer.

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Artificial intelligence (AI), as a research and analysis method, has recently been gaining ground in the ever-evolving scientific field of fire engineering in buildings. Despite the initial delay in utilising machine learning and neural networks due to the shortfall of available computational power, a review of cutting-edge scientific research demonstrates that scientists are now exploring and routinely incorporating such systems in their research processes. As such, a considerable volume of new research is being produced comprising applications of AI in fire engineering. These findings and research questions ought to be summarised, organised, and made accessible for further investigation and refinement. The present study aims to identify recent scientific publications relating to artificial intelligence applications in fire engineering, with particular focus on those tackling the issue of heat transfer through building elements. The method of the meta-narrative review, as implemented in the field of medical advancement research, is discussed, adapted, and finally utilised to weave a narrative that enables the reader to follow the most recent, influential, and impactful works. Efforts are made to uncover trends in the search for heat transfer models and properties under fire loading using AI. The review concludes with our thoughts on how future research can enrich the current findings on heat transfer in buildings exposed to fire actions and elevated temperatures.

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