Using Machine Learning to Improve Fire Safety of Timber Structures
Abstract
Fire safety and fire resistance studies are a vital part of construction. Most conventional fire safety analyses require a combination of computational and experimental methods, making them time-consuming, technically challenging, and financially expensive. By comparison, artificial intelligence-based methods can be computationally more straightforward and more time-efficient, with the added benefit of performing simulation-based tests. This paper focuses on the use of machine learning methods to enhance the understanding and analysis of timber structures in fire. Important works in the field of fire safety and fire resistance for timber structures using artificial intelligence methods are reviewed. The works presented emphasize the importance and accuracy of artificial intelligence and machine learning-based methods in this field.
Reference
Nida Ishtiaq; Thomas W. Loh; Kate T.Q. Nguyen. (2024). Using Machine Learning to Improve Fire Safety of Timber Structures. Industrial Engineering and Management. https://doi.org/10.5772/intechopen.1006895
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