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

Iasonas Bakas,Karolos J Kontoleon

doi:10.3390/app112311435

Iasonas Bakas, Karolos J Kontoleon

Open Access

https://doi.org/10.3390/app112311435

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Journal: Applied Sciences	Publication Date: Dec 2, 2021
Citations: 9	License type: CC BY 4.0

Affiliation: Aristotle University of Thessaloniki

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.

Highlights

The graphs included below allow for a visual interpretation of the impact that varying degrees of input quality have on the performance of the Artificial Neural Networks (ANNs), while the accompanying metrics help quantify the same more accurately
An algorithm capable of predicting the temperature developed on the wall samples’ non-exposed face was eventually constructed, a few items worth highlighting and discussing further appeared during the development and evaluation process. These are listed in the following paragraphs, with the intention to evoke thoughts and discussion regarding potential pitfalls and respective solutions when the ANNs are employed on heat transfer through masonry wall applications
This paper contributes towards the further integration of ANNs in the field of heat transfer through building materials and assemblies

Summary

Introduction

Machine Learning and Artificial Neural Networks, in particular, are increasingly becoming the scientific method of choice for several engineering researchers and practitioners alike. The benefits Artificial Neural Networks (ANNs) can bring to a scientific project make them an attractive method for analysing data. In terms of reliability and performance, it has been shown that, usually, ANNs outperform or are of equivalent accuracy to traditional linear and nonlinear statistical analysis methods [4]. Despite these apparent advantages, the adoption of ANNs in certain research fields, such as heat transfer and fire engineering in the context of civil engineering, is still slow [5]

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