Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92425
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building Environment and Energy Engineering | en_US |
dc.contributor | Research Institute for Sustainable Urban Development | en_US |
dc.creator | Su, LC | en_US |
dc.creator | Wu, X | en_US |
dc.creator | Zhang, X | en_US |
dc.creator | Huang, X | en_US |
dc.date.accessioned | 2022-04-01T01:57:42Z | - |
dc.date.available | 2022-04-01T01:57:42Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/92425 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2021 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | The following publication Su, L.-c., Wu, X., Zhang, X., & Huang, X. (2021). Smart performance-based design for building fire safety: Prediction of smoke motion via AI. Journal of Building Engineering, 43, 102529 is available at https://dx.doi.org/10.1016/j.jobe.2021.102529. | en_US |
dc.rights | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.subject | ASET & RSET | en_US |
dc.subject | Building safety | en_US |
dc.subject | CFD | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Smart firefighting | en_US |
dc.title | Smart performance-based design for building fire safety : prediction of smoke motion via AI | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 43 | en_US |
dc.identifier.doi | 10.1016/j.jobe.2021.102529 | en_US |
dcterms.abstract | The performance-based design (PBD) has been widely adopted for building fire safety over the last three decades, but it requires a laborious and costly process of design and approval. This work presents a smart framework for fire-engineering PBD to predict the smoke motion and the Available Safe Egress Time (ASET) in the atrium by Artificial Intelligence (AI). A CFD database of visibility profile in atrium fires is established, including various fire scenarios, atrium volumes, and ventilation conditions. After the database is trained with the transposed convolutional neural network (TCNN), the AI model can accurately predict the smoke visibility profile and ASET in the atrium fire. Compared to conventional CFD-based PBD by professional fire engineers, AI method provides more consistent and reliable results within a much shorter time. This research verified the feasibility of using AI in fire-engineering PBD, which may reduce the time and cost in creating a fire-safety built environment. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of building engineering, Nov. 2021, v. 43, 102529 | en_US |
dcterms.isPartOf | Journal of building engineering | en_US |
dcterms.issued | 2021-11 | - |
dc.identifier.scopus | 2-s2.0-85105307595 | - |
dc.identifier.eissn | 2352-7102 | en_US |
dc.identifier.artn | 102529 | en_US |
dc.description.validate | 202203 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1249 | - |
dc.identifier.SubFormID | 44339 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Su_Smart_Performance-Based_Design.pdf | Pre-Published version | 2.27 MB | Adobe PDF | View/Open |
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