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).

中文翻译：

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消防工程和科学中的机械信息机器学习和人工智能

火是一种混乱而极端的现象。虽然过去几年见证了集成机器智能 (MI) 以解决并行领域中同样复杂的问题的成功，但我们继续回避利用 MI 来研究火灾行为或评估材料和结构的防火性能。为了倡导使用 MI，这篇评论展示了采用机械信息 MI 来回答消防工程师和科学家面临的一些紧迫问题、多维和不明确的问题的优点。这篇评论还赞同这样一个事实，即传统课程通常不涵盖 MI 的原则，因此它首先引入了许多机器学习 (ML) 和人工智能 (AI) 技术，例如深度学习、元启发式、决策树、随机森林，支持向量机等。然后，本次审查详细介绍了与准备数据库相关的推荐程序，并通过示例进行了适当的 MI 定制火灾分析；使研究人员和从业人员能够轻松实施 MI。在这篇评论的结尾，确定了许多关注点和挑战，以激发感兴趣的读者的好奇心，并加速未来消防工程和科学 (FES) 的研究工作。