Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces
Fire modelling is a common practice in building fire safety analysis, but it is costly. This work develops an AI software, Intelligent Fire Engineering Tool (IFETool), to speed up the fire safety analysis and quickly identify design limits. A big numerical atrium-fire database is firstly formed by c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X22007195 |
| _version_ | 1811315855015804928 |
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| author | Yanfu Zeng Xiaoning Zhang Ling-chu Su Xiqiang Wu Huang Xinyan |
| author_facet | Yanfu Zeng Xiaoning Zhang Ling-chu Su Xiqiang Wu Huang Xinyan |
| author_sort | Yanfu Zeng |
| collection | DOAJ |
| description | Fire modelling is a common practice in building fire safety analysis, but it is costly. This work develops an AI software, Intelligent Fire Engineering Tool (IFETool), to speed up the fire safety analysis and quickly identify design limits. A big numerical atrium-fire database is firstly formed by considering key building and fire parameters. Then, a deep learning model is trained to predict the evolution of tenable smoke visibility, temperature and CO concentration with an accuracy of 97%. The tenability descending profile is further processed to assess the available safe egress time (ASET) and the fire safety of the atriums that have complex roof shapes and slab extensions. This AI design software is able to make a quick assessment of the proposed atrium fire engineering design and give valuable suggestions for potential improvement. Finally, the operation guidelines of IFETool are provided for common design tasks of atrium fire safety. |
| first_indexed | 2024-04-13T11:37:37Z |
| format | Article |
| id | doaj.art-85a771fb0015421d8c7c6a0150f1e789 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-04-13T11:37:37Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-85a771fb0015421d8c7c6a0150f1e7892022-12-22T02:48:23ZengElsevierCase Studies in Thermal Engineering2214-157X2022-12-0140102483Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spacesYanfu Zeng0Xiaoning Zhang1Ling-chu Su2Xiqiang Wu3Huang Xinyan4Dept. of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong KongDept. of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong Kong; Research Institute for Sustainable Urban Development, Hong Kong Polytechnic University, Hong KongDept. of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong Kong; Ove Arup and Partners Hong Kong Limited, Hong KongDept. of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong Kong; The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China; Corresponding author. Dept. of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong Kong.Dept. of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong Kong; Corresponding author.Fire modelling is a common practice in building fire safety analysis, but it is costly. This work develops an AI software, Intelligent Fire Engineering Tool (IFETool), to speed up the fire safety analysis and quickly identify design limits. A big numerical atrium-fire database is firstly formed by considering key building and fire parameters. Then, a deep learning model is trained to predict the evolution of tenable smoke visibility, temperature and CO concentration with an accuracy of 97%. The tenability descending profile is further processed to assess the available safe egress time (ASET) and the fire safety of the atriums that have complex roof shapes and slab extensions. This AI design software is able to make a quick assessment of the proposed atrium fire engineering design and give valuable suggestions for potential improvement. Finally, the operation guidelines of IFETool are provided for common design tasks of atrium fire safety.http://www.sciencedirect.com/science/article/pii/S2214157X22007195Smart buildingFire engineeringIntelligent designDeep learningAI SoftwareAtrium |
| spellingShingle | Yanfu Zeng Xiaoning Zhang Ling-chu Su Xiqiang Wu Huang Xinyan Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces Case Studies in Thermal Engineering Smart building Fire engineering Intelligent design Deep learning AI Software Atrium |
| title | Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces |
| title_full | Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces |
| title_fullStr | Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces |
| title_full_unstemmed | Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces |
| title_short | Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces |
| title_sort | artificial intelligence tool for fire safety design ifetool demonstration in large open spaces |
| topic | Smart building Fire engineering Intelligent design Deep learning AI Software Atrium |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X22007195 |
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