Research status and prospect of fire origin determination based on fire traces

doi:10.16265/j.cnki.issn1003-3033.2024.01.2351

Abstract

Abstract:

In order to help fire investigators determine the location of the fire origin, improve the chain of evidence in accident investigations and identify the cause of the fire quickly and accurately, the researches on fire origin determination based on fire traces were reviewed in present paper. First, fire traces were classified, including burn marks, smoke marks, collapse marks and electrical wiring marks, with emphasis on soot deposition traces. Then, multiple fire origin determination methods at home and abroad were reviewed and classified into three categories: determining the fire origin directly using experience, determining the fire origin using numerical reconstruction techniques, and determining the fire origin using machine learning algorithms. The advantages and limitations of each method were analyzed respectively. Finally, the future research tendency of fire origin determination technology was prospected. The results show that numerical simulation of soot deposition traces combined with machine learning for fire origin determination has a good perspective for application.

Key words: fire origin, fire traces, numerical reconstruction, machine learning, soot deposition traces

CLC Number:

X932爆炸安全与防火、防爆

NIU Tianhui, GENG Dianqiao, YUAN Yi, ZHAO Liang, DONG Hui, WANG Bai. Research status and prospect of fire origin determination based on fire traces[J]. China Safety Science Journal, 2024, 34(1): 238-246.

Figures/Tables 6

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

References 60

[1]	YANG Fan, CAI Zhuoyuan, SU Lei, et al. Research on fire source localization in confined space based on the fire characteristic physical quantity information[J]. International Journal of Metrology and Quality Engineering, 2022, 13:DOI:10.1051/ijmqe/2022001.
[2]	仇俊飞. 建筑火灾调查工作存在的问题及对策[J]. 今日消防, 2022, 7(2):109-111.
	QIU Junfei. Improvements in building fire investigation and strategies[J]. Fire Protection Today, 2022, 7(2):109-111.
[3]	全国商业消防与安全协会. 统计发布/火灾统计/2020年全国火灾数据出炉[OL]. [2021-02-26]. http://www.ncfcsa.org/.
[4]	GORBETT G, CHAPDELAINE W. Scientific method-use, application and gap analysis for origin determination[C]. Plenary Paper Presented at the International Symposium on Fire Investigations, 2014:1-14.
[5]	National Fire Protection Association. NFPA 921: guide for fire and explosion investigations 2014 editio[Z], 2014:921-925.
[6]	袁勇, 王建英. 论火灾调查中对起火部位和起火点的认定[J]. 武警学院学报, 2009, 25(2):81-83.
	YUAN Yong, WANG Jianying. On the identification of fire sites and fire points in fire investigation[J]. Journal of Chinese People's Armed Police Force Academy, 2009, 25(2):81-83.
[7]	LI Nan, LEE E W M, CHEUNG S C P, et al. Multi-fidelity surrogate algorithm for fire origin determination in compartment fires[J]. Engineering with Computers, 2020, 36(3):897-914. doi: 10.1007/s00366-019-00738-9
[8]	GORBETT E G, MECHAM B J, WOOD C B, et al. Structure and evaluation of the process for origin determination in compartment fires[J]. Fire technology, 2017, 53:301-327. doi: 10.1007/s10694-015-0553-3
[9]	孙振文, 石屹, 张冠男, 等. 火灾调查中的关键要素演变及逆向推演[J]. 刑事技术, 2022, 47(3):261-267.
	SUN Zhenwen, SHI Yi, ZHANG Guannan, et al. Key factors about fire investigation: evolving mechanism-based utilization into reverse deduction[J]. Forensic Science and Technology, 2022, 47(3):261-267.
[10]	陈屹, 林震. 烟熏痕迹在火灾调查中的作用[J]. 浙江消防, 2003(10):37-38.
	CHEN Yi, LIN Zhen. The role of smoke marks in fire investigation[J]. Zhejiang Fire, 2003(10): 37-38.
[11]	BUTLER K M, MULHOLLAND G W. Generation and transport of smoke components[J]. Fire Technology, 2004, 40(2):149-176. doi: 10.1023/B:FIRE.0000016841.07530.64
[12]	GORBETT E G, MECHAM B J, WOOD C B, et al. Use of damage in fire investigation: a review of fire patterns analysis, research and future direction[J]. Fire Science Reviews, 2015, 4(1):1-35. doi: 10.1186/s40038-014-0005-z
[13]	张良. 火场通风与火灾烟气痕迹的关联性研究[D]. 天津: 天津商业大学, 2013.
	ZHANG Liang. Research on the relationship between ventilation and smoke patterns in fire[D]. Tianjin: Tianjin University of Commerce, 2013.
[14]	鲁志宝, 梁国福, 杨淳旭. 易燃液体燃烧烟气在空间不同位置的沉积规律[J]. 消防科学与技术, 2011, 30(9):860-862.
	LU Zhibao, LIANG Guofu, YANG Chunxu. Laws of burning flammable liquids smoke depositing at different spatial location[J]. Fire Science and Technology, 2011, 30(9):860-862.
[15]	MENSCH A E, CLEARY T G. Quantifying thermophoretic deposition of soot on surfaces[C]. Suppression, Detection and Signaling Research and Applications Conference, 2017:1-7.
[16]	MENSCH A E, CLEARY T G. Measurements and predictions of thermophoretic soot deposition[J]. International Journal of Heat and Mass Transfer, 2019, 143:DOI: 10.1016/j.ijheatmasstransfer.2019.118444.
[17]	MENSCH A E, CLEARY T G. A soot deposition gauge for fire measurements[R]. NIST Technical Note 1985, 2018.
[18]	RIAHI S, BEYLER C L, HARTMAN J A. Wall smoke deposition from a hot smoke layer[J]. Fire technology, 2013, 49(2): 395-409. doi: 10.1007/s10694-012-0273-x
[19]	RIAHI S. Development of tools for smoke residue and deposition analysis[D]. Washington: The George Washington University, 2010.
[20]	刘旭. 不同火源位置对烟熏痕迹影响的FDS模拟研究[J]. 武警学院学报, 2010, 26(2):80-83.
	LIU Xu. FDS application to smoking trace in fire investigation[J]. Journal of Chinese People's Armed Police Force Academy, 2010, 26(2):80-83.
[21]	王薪宇, 王芸, 张金专, 等. 单室墙角火壁面烟熏痕迹特征研究[J]. 消防科学与技术, 2020, 39(5):727-730.
	WANG Xinyu, WANG Yun, ZHANG Jinzhuan, et al. Research on smoke trace characteristics of corner fire in single room[J]. Fire Science and Technology, 2020, 39(5):727-730.
[22]	徐晓楠, 吴迪, 施照成. 基于FDS的壁面烟熏图痕试验及重构研究[J]. 安全与环境学报, 2014, 14(6):102-106.
	XU Xiaonan, WU Di, SHI Zhaocheng. On the wall smoking pattern experiments and the numerical reconstruction based on FDS[J]. Journal of Safety and Environment, 2014, 14(6):102-106.
[23]	YANG Peizhong, YAO Guangyao, TAN Xun. Modeling carbon black trace in building fire and its validation[J]. Tunnelling and Underground Space Technology, 2015, 49: 35-48. doi: 10.1016/j.tust.2015.03.004
[24]	郑斌, 陈国华. 化工火灾事故起火点推断技术及判据研究[J]. 消防科学与技术, 2011, 30(6):545-550.
	ZHENG Bin, CHEN Guohua. Deduce and determine of fire origin in chemical fire[J]. Fire Science and Technology, 2011, 30(6):545-550.
[25]	郑胜中. 火灾调查中起火点和引火源的认定研究[J]. 今日消防, 2020, 5(8):123-124.
	ZHENG Shengzhong. Identification of ignition originand ignition source in fire investigation[J]. Fire Protection Today, 2020, 5(8):123-124.
[26]	TINSLEY A, GORBETT G E. Fire investigation origin determination survey[J]. Fire and Arson Investigator Journal of the International Association of Arson Investigators, 2013, 63:24-40.
[27]	BYSTROM A, CHENG Xudong, WICKSTROM U, et al. Full-scale experimental and numerical studies on compartment fire under low ambient temperature[J]. Building and Environment, 2012, 51: 255-262. doi: 10.1016/j.buildenv.2011.11.010
[28]	ZHANG Guowei, ZHOU Xiang, ZHU Guoqing, et al. A new accident analysis and investigation model for the complex building fire using numerical reconstruction[J]. Case Studies in Thermal Engineering, 2019, 14:DOI:10.1016/j.csite.2019.100426.
[29]	BRYNER N, MADRZYKOWSK D, GROSSHANDLER W. Reconstructing the station nightclub fire-computer modeling of the fire growth and spread[C]. International Interflam Conference, 11^th Proceedings, 2007: 3-5.
[30]	YUEN A C Y, YEOH G H, ALEXANDER B, et al. Fire scene investigation of an arson fire incident using computational fluid dynamics based fire simulation[J]. Building Simulation, 2014, 7(5):477-487. doi: 10.1007/s12273-014-0164-9
[31]	SHEN Tzusheng, HUANG Yuhsiang, CHIEN Shenwen. Using fire dynamic simulation (FDS) to reconstruct an arson fire scene[J]. Building and Environment, 2008, 43(6): 1036-1045. doi: 10.1016/j.buildenv.2006.11.001
[32]	GORBETT E G, MIFIREE I C. Computer fire models for fire investigation and reconstruction[C]. International Symposium on Fire Investigation and Technology, 2008: 23-34.
[33]	YUEN A C Y, YEOH G H, ALEXANDER R, et al. Fire scene reconstruction of a furnished compartment room in a house fire[J]. Case Studies in Fire Safety, 2014, 1:29-35. doi: 10.1016/j.csfs.2014.01.001
[34]	OVERHOLT K J, EZEKOYE O A. Characterizing heat release rates using an inverse fire modeling technique[J]. Fire Technology, 2012, 48(4): 893-909. doi: 10.1007/s10694-011-0250-9
[35]	SOWAH R A, OFOLI A R, KRAKANI S N, et al. Hardware module design of a real-time multi-sensor fire detection and notification system using fuzzy logic[C]. 2014 IEEE Industry Application Society Annual Meeting. IEEE, 2014: 1-6.
[36]	KRUELL W, SCHULTZE T, TOBERA R, et al. Analysis of dust properties to solve the complex problem of non-fire sensitivity testing of optical smoke detectors[J]. Procedia Engineering, 2013, 62: 859-867. doi: 10.1016/j.proeng.2013.08.136
[37]	FONOLLOSA J, SOLORZANO A, MARCO S. Chemical sensor systems and associated algorithms for fire detection: a review[J]. Sensors, 2018, 18(2):DOI:10.3390/s18020553.
[38]	LIANG Yanhua, TIAN Weimin. Multi-sensor fusion approach for fire alarm using BP neural network[C]. 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS). IEEE, 2016: 99-102.
[39]	刘全义, 朱博, 邓力, 等. 基于机器学习的双参数火灾探测方法[J]. 中国安全科学学报, 2022, 32(5):90-96. doi: 10.16265/j.cnki.issn1003-3033.2022.05.0874
	LIU Quanyi, ZHU Bo, DENG Li, et al. Double parameters fire detection method based on machine learning[J]. China Safety Science Journal, 2022, 32(5):90-96. doi: 10.16265/j.cnki.issn1003-3033.2022.05.0874
[40]	WU Lelong, CHEN Lan, HAO Xiaoran. Multi-sensor data fusion algorithm for indoor fire early warning based on BP neural network[J]. Information, 2021, 12(2): DOI:10.3390/info12020059.
[41]	NAKIP M, GUZELIS C, YILDIZ O. Recurrent trend predictive neural network for multi-sensor fire detection[J]. IEEE Access, 2021, 9: 84 204-84 216. doi: 10.1109/Access.6287639
[42]	BAEK J, ALHINDI T J, JEONG Y S, et al. Real-time fire detection system based on dynamic time warping of multichannel sensor networks[J]. Fire Safety Journal, 2021, 123: DOI:10.1016/j.firesaf.2021.103364.
[43]	KIM H, PARK M, KIM C W, et al. Source localization for hazardous material release in an outdoor chemical plant via a combination of LSTM-RNN and CFD simulation[J]. Computers & Chemical Engineering, 2019, 125: 476-489. doi: 10.1016/j.compchemeng.2019.03.012
[44]	QIAN Fei, CHEN Li, LI Jun, et al. Direct prediction of the toxic gas diffusion rule in a real environment based on LSTM[J]. International Journal of Environmental Research and Public Health, 2019, 16(12): DOI:10.3390/ijerph16122133.
[45]	WU Xiqing, PARK Younggi, LI Ao, et al. Smart detection of fire source in tunnel based on the numerical database and artificial intelligence[J]. Fire Technology, 2021, 57(2): 657-682. doi: 10.1007/s10694-020-00985-z
[46]	KOU Luyao, WANG Xinzhi, GUO Xiaojing, et al. Deep learning based inverse model for building fire source location and intensity estimation[J]. Fire Safety Journal, 2021, 121: DOI:10.1016/j.firesaf.2021.103310.
[47]	祝玉华, 司艺艺, 李智慧. 基于深度学习的烟雾与火灾检测算法综述[J]. 计算机工程与应用, 2022, 58(23):1-11. doi: 10.3778/j.issn.1002-8331.2206-0154
	ZHU Yuhua, SI Yiyi, LI Zhihui, et al. Overview of smoke and fire detection algorithms based on deep learning[J]. Computer Engineering and Applications, 2022, 58(23):1-11. doi: 10.3778/j.issn.1002-8331.2206-0154
[48]	CHAN Tsunghan, JIA Kui, GAO Shenghua, et al. PCANet: a simple deep learning baseline for image classification?[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5017-5032. doi: 10.1109/TIP.2015.2475625 pmid: 26340772
[49]	YANG Jiachen, JIANG Bin, LI Baihua, et al. A fast image retrieval method designed for network big data[J]. IEEE Transactions on Industrial Informatics, 2017, 13(5): 2350-2359. doi: 10.1109/TII.2017.2657545
[50]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Region-based convolutional networks for accurate object detection and segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(1): 142-158. doi: 10.1109/TPAMI.2015.2437384
[51]	ANWAR S, HWANG K, SUNG Wonyong. Fixed point optimization of deep convolutional neural networks for object recognition[C]. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015: 1131-1135.
[52]	PARKHI O M, VEDALDI A, ZISSERMAN A. Deep face recognition[C]. Proceedings of the British Machine Vision Conference 2015, 2015:1-10.
[53]	张苗, 李璞, 杨漪, 等. 基于目标检测卷积神经网络的图像型火灾探测算法[J]. 消防科学与技术, 2022, 41(6):807-811.
	ZHANG Miao, LI Pu, YANG Yi, et al. Image fire detection algorithms based on object detection convolutional neural networks[J]. Fire Science and Technology, 2022, 41(6):807-811.
[54]	FRIZZI S, KAABI R, BOUCHOUICHA M, et al. Convolutional neural network for video fire and smoke detection[C]. IECON 2016-42^nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016: 877-882.
[55]	XU Zhaoyi, GUO Yanjie, SALEH J H. Tackling small data challenges in visual fire detection: a deep convolutional generative adversarial network approach[J]. IEEE Access, 2020, 9:3936-3946. doi: 10.1109/Access.6287639
[56]	YIN Zhijian, WAN Boyang, YUAN Feiniu, et al. A deep normalization and convolutional neural network for image smoke detection[J]. Ieee Access, 2017, 5:18429-18 438.
[57]	GOTTHANS J, GOTTHANS T, MARSALEK R. Deep convolutional neural network for fire detection[C]. 2020 30^th International Conference Radioelektronika. IEEE, 2020:1-6.
[58]	WANG Wen, LI Tianping. Fire video image detection based on a convolutional neural network[J]. Journal of Physics: Conference Series, 2020, 1453(1): DOI:10.1088/1742-6596/1453/1/012161.
[59]	KURZAWSKI A, CABRERA J M, EZEKOYE O A. Model considerations for fire scene reconstruction using a Bayesian framework[J]. Fire Technology, 2020, 56(2):445-467. doi: 10.1007/s10694-019-00886-w
[60]	OVERHOLT K J, EZEKOYE O A. Quantitative testing of fire scenario hypotheses: a Bayesian inference approach[J]. Fire Technology, 2015, 51(2):335-367. doi: 10.1007/s10694-013-0384-z