Performance Evaluation of Artificial Neural Networks (ANN) Predicting Heat Transfer through Masonry Walls Exposed to Fire

Abstract

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.

Keywords

• Machine Learning
• Artificial Neural Networks
• ANN performance
• fire action
• masonry wall heat transfer
• prediction evaluation