Abstract
The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.
Funder
Australian Research Council
Subject
Chemistry (miscellaneous),Analytical Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Molecular Medicine,Drug Discovery,Pharmaceutical Science
Reference102 articles.
1. ISO 5660-1: Reaction-to-Fire Tests-Heat Release, Smoke Production and Mass Loss Rate-Part 1:(Cone Calorimeter Method),2002
2. Computational Fluid Dynamics: A Practical Approach;Tu,2018
3. Deep Residual Learning for Image Recognition
4. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Cited by
19 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献