Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92443
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.contributor | Mainland Development Office | en_US |
dc.creator | Wang, Z | en_US |
dc.creator | Zhang, T | en_US |
dc.creator | Wu, X | en_US |
dc.creator | Huang, X | en_US |
dc.date.accessioned | 2022-04-01T01:57:48Z | - |
dc.date.available | 2022-04-01T01:57:48Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/92443 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2021 Elsevier Ltd. All rights reserved. | 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.rights | The following publication Wang, Z., Zhang, T., Wu, X., & Huang, X. (2022). Predicting transient building fire based on external smoke images and deep learning. Journal of Building Engineering, 47, 103823 is available at https://dx.doi.org/10.1016/j.jobe.2021.103823. | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Compartment fire model | en_US |
dc.subject | Fire recognition | en_US |
dc.subject | Smart firefighting | en_US |
dc.title | Predicting transient building fire based on external smoke images and deep learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 47 | en_US |
dc.identifier.doi | 10.1016/j.jobe.2021.103823 | en_US |
dcterms.abstract | A real-time evaluation of fire severity inside a building could facilitate decision-making in firefighting and rescue operations. This work explores the real-time prediction of transient fire scenarios by using external smoke images and deep learning algorithms. A big database of 1845 simulated compartment fire scenarios is formed. Three input parameters (constant fire heat release rate, opening size, and fuel type) are paired with the external smoke images, and then trained by Convolutional Neural Network (CNN) model. Results show that by training either the front-view or side-view smoke images, the artificial intelligence (AI) method can well identify the transient fire heat release rate inside the building, even without knowing the burning fuels, and the error is no more than 20%. This work demonstrates that the deep learning algorithms can be trained with simulated smoke images to determine the hidden fire information in real-time and shows great potential in smart firefighting applications. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of building engineering, 15 Apr. 2022, v. 47, 103823 | en_US |
dcterms.isPartOf | Journal of building engineering | en_US |
dcterms.issued | 2022-04-15 | - |
dc.identifier.scopus | 2-s2.0-85121234381 | - |
dc.identifier.eissn | 2352-7102 | en_US |
dc.identifier.artn | 103823 | en_US |
dc.description.validate | 202203 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1251 | - |
dc.identifier.SubFormID | 44362 | - |
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 | |
---|---|---|---|---|
Wang_Predicting_Transient_Building.pdf | Pre-Published version | 2.8 MB | Adobe PDF | View/Open |
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