Abstract Tunnel fire is one of the most severe global fire hazards and causes a significant amount of economic losses and casualties every year. Over the last 50 years, numerous full-scale and reduced-scale tunnel fire tests, as well as numerical simulations have been conducted to quantify the critical fire events and key parameters to guide the fire safety design of the tunnel. In light of the recent advances in big data and artificial intelligence, this paper aims to establish a database that contains all existing experimental data of tunnel fire, based on an extensive literature review on tunnel fire tests. This tunnel-fire database summarizes seven key parameters of flame, ventilation, and smoke in that is open access at a GitHub site: https://github.com/PolyUFire/Tunnel_Fire_Database . The test conditions, experimental phenomena, and data of each literature work were organized and categorized in a standard format that could be conveniently accessed and continuously updated. Based on this database, machine learning is applied to predict the critical ventilation velocity of a tunnel fire as a demonstration. The review of the current database not only reveals more valuable information and hidden problems in the conventional collection of test data, but also provides new directions in future tunnel fire research. The established database and methodology help promote the application of artificial intelligence and smart firefighting in tunnel fire safety.

中文翻译：

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大实验数据库与人工智能在隧道火灾研究中的展望

摘要 隧道火灾是全球最严重的火灾隐患之一，每年造成大量经济损失和人员伤亡。在过去的 50 年中，已经进行了大量的全尺寸和缩小尺寸的隧道火灾试验以及数值模拟，以量化关键火灾事件和关键参数，以指导隧道的消防安全设计。鉴于大数据和人工智能的最新进展，本文旨在基于对隧道火灾试验的广泛文献综述，建立一个包含所有现有隧道火灾试验数据的数据库。这个隧道火灾数据库总结了火焰、通风和烟雾的七个关键参数，在 GitHub 站点上开放访问：https://github.com/PolyUFire/Tunnel_Fire_Database。测试条件、实验现象、每个文献作品的数据都按照标准格式进行组织和分类，可以方便地访问和不断更新。基于该数据库，应用机器学习来预测隧道火灾的临界通风速度作为示范。对当前数据库的回顾不仅揭示了常规测试数据收集中更多有价值的信息和隐藏的问题，而且为未来隧道火灾研究提供了新的方向。建立的数据库和方法论有助于推动人工智能和智慧消防在隧道消防安全中的应用。作为示范，机器学习用于预测隧道火灾的临界通风速度。对当前数据库的回顾不仅揭示了常规测试数据收集中更多有价值的信息和隐藏的问题，而且为未来隧道火灾研究提供了新的方向。建立的数据库和方法论有助于推动人工智能和智慧消防在隧道消防安全中的应用。作为示范，机器学习用于预测隧道火灾的临界通风速度。对当前数据库的回顾不仅揭示了常规测试数据收集中更多有价值的信息和隐藏的问题，而且为未来隧道火灾研究提供了新的方向。建立的数据库和方法论有助于推动人工智能和智慧消防在隧道消防安全中的应用。