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A platform for research: civil engineering, architecture and urbanism
Perspectives of big experimental database and artificial intelligence in tunnel fire research
Graphical abstract Display Omitted
Highlights Review the history, methods, and key parameters of tunnel fire research. Establish an experimental database of full-scale and model-scale tunnel fire tests. Database is open access online and available for updating by the fire community. Application of machine learning and experimental database to predict tunnel fire.
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.
Perspectives of big experimental database and artificial intelligence in tunnel fire research
Graphical abstract Display Omitted
Highlights Review the history, methods, and key parameters of tunnel fire research. Establish an experimental database of full-scale and model-scale tunnel fire tests. Database is open access online and available for updating by the fire community. Application of machine learning and experimental database to predict tunnel fire.
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.
Perspectives of big experimental database and artificial intelligence in tunnel fire research
Zhang, Xiaoning (author) / Wu, Xiqiang (author) / Park, Younggi (author) / Zhang, Tianhang (author) / Huang, Xinyan (author) / Xiao, Fu (author) / Usmani, Asif (author)
2020-10-25
Article (Journal)
Electronic Resource
English
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