| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Building Services Engineering | en_US |
| dc.creator | Wu, X | en_US |
| dc.creator | Zhang, X | en_US |
| dc.creator | Huang, X | en_US |
| dc.creator | Xiao, F | en_US |
| dc.creator | Usmani, A | en_US |
| dc.date.accessioned | 2021-04-13T06:08:20Z | - |
| dc.date.available | 2021-04-13T06:08:20Z | - |
| dc.identifier.issn | 1996-3599 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/89585 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Tsinghua University Press, co-published with Springer | en_US |
| dc.subject | CFD | en_US |
| dc.subject | Critical event | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | LSTM/TCNN | en_US |
| dc.subject | Smart firefighting | en_US |
| dc.subject | Tunnel fires | en_US |
| dc.title | A real-time forecast of tunnel fire based on numerical database and artificial intelligence | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.doi | 10.1007/s12273-021-0775-x | en_US |
| dcterms.abstract | The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters. To alleviate the potential casualties, fast while reasonable decisions should be made for rescuing, based on the timely prediction of fire development in tunnels. This paper targets to achieve a real-time prediction (within 1 s) of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence (AI) methods. A CFD database of 100 simulated tunnel fire scenarios under various fire location, fire size, and ventilation condition is established. The proposed AI model combines a Long Short-term Memory (LSTM) model and a Transpose Convolution Neural Network (TCNN). The real-time ceiling temperature profile and thousands of temperature-field images are used as the training input and output. Results show that the predicted temperature field 60 s in advance achieves a high accuracy of around 97%. Also, the AI model can quickly identify the critical temperature field for safe evacuation (i.e., a critical event) and guide emergency responses and firefighting activities. This study demonstrates the promising prospects of AI-based fire forecasts and smart firefighting in tunnel spaces. | en_US |
| dcterms.accessRights | embargoed access | en_US |
| dcterms.bibliographicCitation | Building simulation, Published: 09 March 2021, p. 1-14, https://doi.org/10.1007/s12273-021-0775-x | en_US |
| dcterms.isPartOf | Building simulation | en_US |
| dcterms.issued | 2021 | - |
| dc.identifier.scopus | 2-s2.0-85102348319 | - |
| dc.description.validate | 202104 bcvc | en_US |
| dc.description.oa | Not applicable | en_US |
| dc.identifier.FolderNumber | a0699-n08 | en_US |
| dc.identifier.SubFormID | 1028 | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | Theme-based Research Scheme (T22-505/19-N) | en_US |
| dc.description.fundingText | PolyU Emerging Frontier Area (EFA) Scheme of RISUD (P0013879) | en_US |
| dc.description.pubStatus | Early release | en_US |
| dc.date.embargo | 0000-00-00 (to be updated) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
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