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Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection

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  • Sun, Bin
  • Li, Yan
  • Zhang, Yangyang
  • Guo, Tong

Abstract

Diverse and complex fire environment in modern utility tunnels with multiple uncertainties make fire detection difficult to be achieved accurately. This study aims to develop an intelligent fire detection technique to address the difficulty. In the technique, initially, a lightweight image segmentation method is proposed for initial estimation of the fire source location. Then, the multi-source heterogeneous data fusion fire detection is implemented for fire source localization and ceiling temperature distribution prediction based on Gauss model and the improved multi-particle swarm optimization (MPSO) algorithm. Additionally, the results of the case study support the ability of the intelligent fire detection technique through compared with the experiment results and the previous methods, which can be used to achieve precise and stable fire source localization and ceiling temperature prediction in the utility tunnel fire.

Suggested Citation

  • Sun, Bin & Li, Yan & Zhang, Yangyang & Guo, Tong, 2024. "Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:reensy:v:248:y:2024:i:c:s095183202400228x
    DOI: 10.1016/j.ress.2024.110154
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    References listed on IDEAS

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    1. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Yang, Guan ding & Liu, Jie & Wang, Wan qing & Zhou, Hao wen & Wang, Xiao dong & Lu, Feng & Wan, Li ting & Teng, Liang yun & Zhao, Huyun, 2023. "Integration of the BBN-NK-Boltzmann model of tunnel fire network scenarios with coupled forward and reverse rendition analysis," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    3. He, Yuxuan & Su, Huai & Zio, Enrico & Peng, Shiliang & Fan, Lin & Yang, Zhaoming & Yang, Zhe & Zhang, Jinjun, 2023. "A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Li, Qilin & Wang, Yang & Chen, Wensu & Li, Ling & Hao, Hong, 2024. "Machine learning prediction of BLEVE loading with graph neural networks," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Fan, Shiqi & Yang, Zaili, 2023. "Towards objective human performance measurement for maritime safety: A new psychophysiological data-driven machine learning method," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Hai, Nan & Gong, Daqing & Dai, Zixuan, 2024. "Target spectrum-based risk analysis model for utility tunnel O&M in multiple scenarios and its application," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Wu, Jiansong & Bai, Yiping & Fang, Weipeng & Zhou, Rui & Reniers, Genserik & Khakzad, Nima, 2021. "An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    8. Chunyan, Ling & Jingzhe, Lei & Way, Kuo, 2022. "Bayesian support vector machine for optimal reliability design of modular systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    9. Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    10. Hai, Nan & Gong, Daqing & Liu, Shifeng & Dai, Zixuan, 2022. "Dynamic coupling risk assessment model of utility tunnels based on multimethod fusion," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
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