Intelligent prediction method for fire temperature fields in underground exhibition spaces of high-intensity urban areas
The detection of fire temperature fields in underground exhibition spaces has become a critical issue for fire evacuation planning. This study aims to elucidate the influence mechanisms of spatial characteristics on fire temperature fields and innovatively proposes a temperature field prediction met...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X2500471X |
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| _version_ | 1850238089695330304 |
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| author | Yuan Shi Yang Zhou Guanhua Qu Lan Wang Rong Wang Zenghui Liu |
| author_facet | Yuan Shi Yang Zhou Guanhua Qu Lan Wang Rong Wang Zenghui Liu |
| author_sort | Yuan Shi |
| collection | DOAJ |
| description | The detection of fire temperature fields in underground exhibition spaces has become a critical issue for fire evacuation planning. This study aims to elucidate the influence mechanisms of spatial characteristics on fire temperature fields and innovatively proposes a temperature field prediction method based on distributed fiber optic temperature sensors. The results indicate that different exhibition space layouts and heights significantly affect the temperature field distribution of key fire prevention planes, with the impact of space heights being more significant than that of layouts. Based on this, the Fire Dynamics Simulator (FDS) platform was used to simulate the fire temperature fields under different conditions, constructing fire temperature databases. Through a comparative selection of multiple algorithms, it was found that the RF prediction model with 41 input features performs best in terms of accuracy and applicability, with the Mean Absolute Errors (MAE) values of 1.09 °C and 0.52 °C for fire rooms and no-fire rooms, respectively, and the Mean Absolute Percentage Errors (MAPE) values of 1.11 % and 0.68 %, respectively. Finally, a multi-feature generalization test was conducted to verify the model's good generalization performance in new scenes. This study provides technical support with application prospects for fire evacuation in underground exhibition spaces. |
| format | Article |
| id | doaj-art-4318fc73df5a48ff85f16a4cc418b2a1 |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-4318fc73df5a48ff85f16a4cc418b2a12025-08-20T02:01:34ZengElsevierCase Studies in Thermal Engineering2214-157X2025-07-017110621110.1016/j.csite.2025.106211Intelligent prediction method for fire temperature fields in underground exhibition spaces of high-intensity urban areasYuan Shi0Yang Zhou1Guanhua Qu2Lan Wang3Rong Wang4Zenghui Liu5School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China; State Key Laboratory of Subtropical Building and Urban Science, Guangzhou, 510640, ChinaTianjin Key Laboratory of Architectural Physical Environment and Ecological Technologies, Tianjin University, Tianjin, 300072, China; School of Architecture, Tianjin University, Tianjin, 300072, ChinaTianjin Key Laboratory of Architectural Physical Environment and Ecological Technologies, Tianjin University, Tianjin, 300072, China; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China; Corresponding author. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.Tianjin Fire Science and Technology Research Institute of MEM, Tianjin, 300381, ChinaSchool of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, ChinaTianjin Key Laboratory of Architectural Physical Environment and Ecological Technologies, Tianjin University, Tianjin, 300072, China; School of Future Technology, Tianjin University, Tianjin, 300072, China; Corresponding author. School of Future Technology, Tianjin University, Tianjin, China.The detection of fire temperature fields in underground exhibition spaces has become a critical issue for fire evacuation planning. This study aims to elucidate the influence mechanisms of spatial characteristics on fire temperature fields and innovatively proposes a temperature field prediction method based on distributed fiber optic temperature sensors. The results indicate that different exhibition space layouts and heights significantly affect the temperature field distribution of key fire prevention planes, with the impact of space heights being more significant than that of layouts. Based on this, the Fire Dynamics Simulator (FDS) platform was used to simulate the fire temperature fields under different conditions, constructing fire temperature databases. Through a comparative selection of multiple algorithms, it was found that the RF prediction model with 41 input features performs best in terms of accuracy and applicability, with the Mean Absolute Errors (MAE) values of 1.09 °C and 0.52 °C for fire rooms and no-fire rooms, respectively, and the Mean Absolute Percentage Errors (MAPE) values of 1.11 % and 0.68 %, respectively. Finally, a multi-feature generalization test was conducted to verify the model's good generalization performance in new scenes. This study provides technical support with application prospects for fire evacuation in underground exhibition spaces.http://www.sciencedirect.com/science/article/pii/S2214157X2500471XUnderground exhibition space fireDistributed fiber optic sensorsTemperature field detectionMachine learning |
| spellingShingle | Yuan Shi Yang Zhou Guanhua Qu Lan Wang Rong Wang Zenghui Liu Intelligent prediction method for fire temperature fields in underground exhibition spaces of high-intensity urban areas Case Studies in Thermal Engineering Underground exhibition space fire Distributed fiber optic sensors Temperature field detection Machine learning |
| title | Intelligent prediction method for fire temperature fields in underground exhibition spaces of high-intensity urban areas |
| title_full | Intelligent prediction method for fire temperature fields in underground exhibition spaces of high-intensity urban areas |
| title_fullStr | Intelligent prediction method for fire temperature fields in underground exhibition spaces of high-intensity urban areas |
| title_full_unstemmed | Intelligent prediction method for fire temperature fields in underground exhibition spaces of high-intensity urban areas |
| title_short | Intelligent prediction method for fire temperature fields in underground exhibition spaces of high-intensity urban areas |
| title_sort | intelligent prediction method for fire temperature fields in underground exhibition spaces of high intensity urban areas |
| topic | Underground exhibition space fire Distributed fiber optic sensors Temperature field detection Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X2500471X |
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