Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/99716
DC Field | Value | Language |
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dc.contributor | Department of Building Environment and Energy Engineering | - |
dc.contributor | Research Institute for Sustainable Urban Development | - |
dc.creator | Zeng, Y | en_US |
dc.creator | Zhang, X | en_US |
dc.creator | Su, LC | en_US |
dc.creator | Wu, X | en_US |
dc.creator | Huang, X | en_US |
dc.date.accessioned | 2023-07-19T00:54:33Z | - |
dc.date.available | 2023-07-19T00:54:33Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/99716 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.rights | © 2022 The Authors. Published by Elsevier Ltd. | en_US |
dc.rights | This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | en_US |
dc.rights | The following publicationZeng, Y., Zhang, X., Su, L. -., Wu, X., & Xinyan, H. (2022). Artificial intelligence tool for fire safety design (IFETool): Demonstration in large open spaces. Case Studies in Thermal Engineering, 40, 102483 is available at https://doi.org/10.1016/j.csite.2022.102483. | en_US |
dc.subject | Smart building | en_US |
dc.subject | Fire engineering | en_US |
dc.subject | Intelligent design | en_US |
dc.subject | Deep learning | en_US |
dc.subject | AI Software | en_US |
dc.subject | Atrium | en_US |
dc.title | Artificial Intelligence tool for fire safety design (IFETool) : demonstration in large open spaces | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 40 | en_US |
dc.identifier.doi | 10.1016/j.csite.2022.102483 | en_US |
dcterms.abstract | 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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Case studies in thermal engineering, Dec. 2022, v. 40, 102483 | en_US |
dcterms.isPartOf | Case studies in thermal engineering | en_US |
dcterms.issued | 2022-12 | - |
dc.identifier.scopus | 2-s2.0-85140257856 | - |
dc.identifier.eissn | 2214-157X | en_US |
dc.identifier.artn | 102483 | en_US |
dc.description.validate | 202307 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | PolyU Emerging Frontier Area; PolyU Start-up Fund; RISUD; Educational Foundation of America; National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Zeng_Artificial_Intelligence_Tool.pdf | 9.94 MB | Adobe PDF | View/Open |
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