Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92442
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building Environment and Energy Engineering | en_US |
dc.contributor | Mainland Development Office | en_US |
dc.contributor | Research Institute for Sustainable Urban Development | en_US |
dc.creator | Wu, X | en_US |
dc.creator | Zhang, X | en_US |
dc.creator | Jiang, Y | en_US |
dc.creator | Huang, X | en_US |
dc.creator | Huang, GGQ | en_US |
dc.creator | Usmani, A | en_US |
dc.date.accessioned | 2022-04-01T01:57:48Z | - |
dc.date.available | 2022-04-01T01:57:48Z | - |
dc.identifier.issn | 0886-7798 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/92442 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | 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 Wu, X., Zhang, X., Jiang, Y., Huang, X., Huang, G. G. Q., & Usmani, A. (2022). An intelligent tunnel firefighting system and small-scale demonstration. Tunnelling and Underground Space Technology, 120, 104301 is available at https://dx.doi.org/10.1016/j.tust.2021.104301. | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Fire modelling | en_US |
dc.subject | IoT system | en_US |
dc.subject | Smart firefighting | en_US |
dc.subject | Tunnel fire prediction | en_US |
dc.title | An intelligent tunnel firefighting system and small-scale demonstration | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 120 | en_US |
dc.identifier.doi | 10.1016/j.tust.2021.104301 | en_US |
dcterms.abstract | Disastrous fire event in the confined tunnel is a fatal hazard, threatening the lives of trapped people and firefighters. Considering the rapid development of fire and the complex environment of tunnels, an accurate and timely fire identification system is in urgent need for guiding the evacuation, rescue, and firefighting actions. This study proposes an intelligent system and digital twin composed of four main components to collect, manage, process and visualize the tunnel fire information. As demonstrated in a laboratory-scale tunnel model, the AI model is trained with a large numerical database to successfully identify the fire size and location. The whole system is assessed in terms of accuracy, timeliness and robustness. The AI model attained an overall accuracy of 98% in predicting the tunnel fire scenarios. The total time delay is around 1 s from the on-site measurement of temperature to the final display of the tunnel fire scenario on a remote user interface. Moreover, the system is robust enough to predict fire, even if part of the temperature sensors is failed or destroyed by fire. The proposed intelligent system will be a valuable step for smart firefighting from the concept to practice. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Tunnelling and underground space technology, Feb. 2022, v. 120, 104301 | en_US |
dcterms.isPartOf | Tunnelling and underground space technology | en_US |
dcterms.issued | 2022-02 | - |
dc.identifier.scopus | 2-s2.0-85121222760 | - |
dc.identifier.artn | 104301 | en_US |
dc.description.validate | 202203 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1251 | - |
dc.identifier.SubFormID | 44361 | - |
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 | |
---|---|---|---|---|
Wu_Intelligent_Tunnel_Firefighting.pdf | Pre-Published version | 2.82 MB | Adobe PDF | View/Open |
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