Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences

Fire is a chaotic and extreme phenomenon. While the past few years have witnessed the success of integrating machine intelligence (MI) to tackle equally complex problems in parallel fields, we continue to shy away from leveraging MI to study fire behavior or to evaluate fire performance of materials and structures. In order to advocate for the use of MI, this review showcases the merit of adopting mechanistically-informed MI to answer some of the burning questions, multi-dimensional and ill-defined problems fire engineers and scientists are facing. This review also sympathizes with the fact that a traditional curriculum does not often cover principles of MI and hence it starts by introducing a number of machine learning (ML) and artificial intelligence (AI) techniques such as deep learning, metaheuristics, decision trees, random forest, support vector machines etc. Then, this review details recommended procedures associated with preparing databases and carrying out a proper MI-tailored fire analysis via examples; to enable researchers and practitioners from implementing MI with ease. Towards the end of this review, a number of concerns and challenges are identified to stimulate the curiosity of interested readers and accelerate future research works within fire engineering and sciences (FES).

Automated summary

This review discusses the importance of adopting mechanistically-informed machine intelligence (MI) to study fire behavior and evaluate fire performance of materials and structures. It also introduces various machine learning and artificial intelligence techniques for fire analysis, providing recommended procedures and examples. Challenges and concerns are also identified to stimulate curiosity and accelerate future research in fire engineering and sciences.