Meta-Narrative Review of Artificial Intelligence Applications in Fire Engineering with Special Focus on Heat Transfer through Building Elements

Abstract

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.

Keywords

• artificial intelligence
• machine learning
• neural networks
• fire engineering
• heat transfer
• building