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Study on prediction of temperature characteristic parameters for subway train with multiple lateral openings and tnnnels
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Zhenkun WU1, 2, Min PENG**, 2, Guoqing ZHU2, Lu LIU2, Dongzi QIN3
China Safety Science Journal | 2026, 36(1) : 130 - 137
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China Safety Science Journal | 2026, 36(1): 130-137
• Safety Technology and Engineering •
Study on prediction of temperature characteristic parameters for subway train with multiple lateral openings and tnnnels
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Zhenkun WU1, 2, Min PENG**, 2, Guoqing ZHU2, Lu LIU2, Dongzi QIN3
Affiliations
  • 1School of Emergency Management, Wuxi University, Wuxi Jiangsu 214105, China
  • 2School of Safety Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China
  • 3State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei Anhui 230026, China
Published: 2026-01-28 doi: 10.16265/j.cnki.issn1003-3033.2026.01.1196
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Existing methods for predicting temperature from subway carriage and tunnel fires mainly rely on physical models and empirical methods that are valid only under narrowly-defined environmental conditions. To solve this issue, this study adopts an artificial-intelligence-based approach. A GA-BPNN network model is constructed by optimizing BPNN using a GA. The GA is employed to global optimize BPNN's weights and thresholds, after which the model is trained to predict the temperature distribution of both the subway carriage and the tunnel, thereby achieving intelligent inversion of the fire temperature field. The results show that, for subway carriage temperature prediction, GA-BPNN model yields a mean absolute error (MAE) of 8.17, a root mean square error (RMSE) of 9.76, and a coefficient of determination (R2) of 0.99. For tunnel temperature prediction, MAE is 3.95, RMSE is 5.63, and R2 reaches 0.98. By comparing the results with those of the traditional BPNN, it is found that the GA-BPNN model outperforms the conventional BPNN in both prediction accuracy and generalization capability.

multiple lateral openings  /  subway train  /  temperature prediction  /  characteristic parameters  /  back-propagation neural network(BPNN)  /  genetic algorithm(GA)
吴振坤讲师, 彭敏副教授, 朱国庆教授, 刘璐, 秦东子. 侧向多开口地铁列车和隧道温度特征参数预测. 中国安全科学学报, 2026 , 36 (1) : 130 -137 . DOI: 10.16265/j.cnki.issn1003-3033.2026.01.1196
Zhenkun WU, Min PENG, Guoqing ZHU, Lu LIU, Dongzi QIN. Study on prediction of temperature characteristic parameters for subway train with multiple lateral openings and tnnnels[J]. China Safety Science Journal, 2026 , 36 (1) : 130 -137 . DOI: 10.16265/j.cnki.issn1003-3033.2026.01.1196
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Year 2026 volume 36 Issue 1
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.01.1196
  • Receive Date：2025-09-05
  • Online Date：2026-07-08
  • Published：2026-01-28
Article Data
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History
  • Received：2025-09-05
  • Revised：2025-11-22
Funding
Affiliations
    1School of Emergency Management, Wuxi University, Wuxi Jiangsu 214105, China
    2School of Safety Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China
    3State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei Anhui 230026, China
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