主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2024, Vol. 43 ›› Issue (9): 1292-1297.

• • 上一篇    下一篇

基于深度学习的隧道火灾火源位置和热释放速率反演

蒋立1, 何廷全2, 郭鑫1, 阳东1   

  1. （1. 重庆大学 土木工程学院，重庆 400045；2. 广西新发展交通集团有限公司，广西 南宁 530000）
  • 出版日期:2024-09-15 发布日期:2024-09-15
  • 作者简介:蒋 立（2000— ），男，四川德阳人，重庆大学土木工程学院硕士研究生，主要从事隧道火灾方面的研究，重庆市沙坪坝区沙正街174号重庆大学B区，400045。
  • 基金资助:
    国家重点研发计划项目（2021YFC3002000）；广西科技计划项目（桂科AB19110019）

Fire source location and heat release rate inversion in tunnel fires based on deep learning

Jiang Li1, He Tingquan2, Guo Xin1, Yang Dong1   

  1. (1. China School of Civil Engineering, Chongqing University, Chongqing 400045, China;2. Guangxi Xinfazhan Communications Group Co., Ltd., Guangxi Nanning 530000, China)
  • Online:2024-09-15 Published:2024-09-15

摘要： 火源位置和热释放速率（HRR）是指导隧道火灾消防应急救援的重要信息。但在实际中，得到的关于火场的信息十分有限，很难直接得到火源关键信息。提出了一种基于深度学习的隧道火灾火源位置和HRR的反演方法。首先，基于试验数据验证的数值模拟建立不同边界条件下的隧道火灾数据集。基于卷积神经网络（CNN）和长短期记忆网络（LSTM）建立有限的固定式传感器温度数据和火源位置及HRR之间的映射关系，评估了该模型对于火源位置和HRR的反演效果。评估了时间输入步长和传感器间距对该模型反演性能的影响。结果表明，该模型对HRR和火源位置都有较好的反演性能，当时间输入步长为20 s，传感器间距为30 m时，模型反演HRR和火源位置的R2值分别为0.97和0.99。

关键词: 隧道火灾, 数值模拟, 火灾试验, 长短期记忆网络, 热释放速率, 深度学习

Abstract: The fire source location and heat release rate （HRR） are crucial information guiding emergency firefighting and rescue perations during tunnel fires. However, in practice, the information that can be obtained about the fire is limited. It is difficult to get the fire source key parameters directly. Therefore, we researched the deep learning-based method for inversing thefire source location and HRR in tunnel fires. Firstly, a tunnel fire dataset under different boundary conditions is established based on numerical simulations validated by experimental data. Based on convolutional neural network （CNN） and long short-term memory network （LSTM）, the mapping relationship between fixed temperature sensor data and fire source location and HRR was established. The inversion effectiveness of the model for fire source parameters was evaluated. And the effect of time series length and sensor spacing on the inversion effectiveness were evaluated. The results demonstrate that the model has good inversion performance for both HRR and fire source location. When the time series length was 20 s and the sensor spacing was 30 m, the R2 values of the model inversion for HRR and fire location are 0.97 and 0.99, respectively.

Key words: tunnel fire, numerical simulation, fire tests, long and short-term memory network, heat release rate, deep learning