LI Heng, MA Guorui, LIU Yudi, ZHANG Haiming. A remote sensing imagery-based model for assessment of building damage induced by large-equivalent explosions[J]. Explosion And Shock Waves, 2024, 44(3): 031407. doi: 10.11883/bzycj-2023-0331
Citation: LI Heng, MA Guorui, LIU Yudi, ZHANG Haiming. A remote sensing imagery-based model for assessment of building damage induced by large-equivalent explosions[J]. Explosion And Shock Waves, 2024, 44(3): 031407. doi: 10.11883/bzycj-2023-0331

A remote sensing imagery-based model for assessment of building damage induced by large-equivalent explosions

doi: 10.11883/bzycj-2023-0331
  • Received Date: 2023-09-15
  • Rev Recd Date: 2024-02-01
  • Available Online: 2024-02-04
  • Publish Date: 2024-03-14
  • To address challenges in the field of large-scale explosive building damage assessment, where the explosion process is too complex for high-precision numerical simulation, and relying solely on change detection from remote sensing imagery cannot capture detailed internal information and lacks the capability of predicting in advance, this paper establishes a building damage assessment model for large-scale explosive events by coupling empirical mechanics models with remote sensing image interpretation and big data analysis. The study initially constructs a damage dataset based on specific historical cases of large-scale explosions. This involves extracting building damage information (including building types and damage levels) from remote sensing imagery and supplementing damage details with additional big data sources such as collected online images, videos, and news reports to enhance the precision of the sampled data. Geographic information systems spatial analysis is employed to digitize the damage information, obtaining data on building types, damage levels, and the distance from the target building to the explosion center, forming the damage dataset. Subsequently, the empirical model parameters are refined based on the training samples from the damage dataset, creating damage assessment models applicable to different building types for large-scale explosive events. The performance of the model is then tested using validation samples from the damage dataset. Experimental results demonstrate a model fitting goodness of over 96%, accuracy on validation samples exceeding 84%, and an overall error within an acceptable range. The model, under certain accuracy requirements, can provide guidance for site selection of storage locations for chemicals and hazardous materials, emergency evacuation of people in the event of a risk of large-scale explosions, critical equipment evacuation during an emergency, resource dispatching for rescue and relief after an accident, and building damage assessment.
  • [1]
    张连玉, 汪令羽, 苗瑞生. 爆炸气体动力学基础 [M]. 北京: 北京工业学院出版社, 1987: 425–426.
    [2]
    周子钦. 基于多时相图像的打击效果评估技术研究 [D]. 武汉: 华中科技大学, 2020: 40–45. DOI: 10.27157/d.cnki.ghzku.2020.006392.

    ZHOU Z Q. Study of damage effect assessment based on multi-temporal image [D]. Wuhan: Huazhong University of Science and Technology, 2020: 40–45. DOI: 10.27157/d.cnki.ghzku.2020.006392.
    [3]
    张军. 多时相图像检测方法及其在毁伤评估系统中的应用 [D]. 上海: 上海交通大学, 2008: 17–18.

    ZHANG J. Processing method of multi-temporal remote sensing images and the use in damage assessment [D]. Shanghai: Shanghai Jiao Tong University, 2008: 17–18.
    [4]
    SIRMACEK B, UNSALAN C. Damaged building detection in aerial images using shadow information [C]//Proceedings of the 4th International Conference on Recent Advances in Space Technologies. Istanbul: IEEE, 2009: 249–252. DOI: 10.1109/RAST.2009.5158206.
    [5]
    王威. 基于图像理解的打击效果评估系统研究与实现 [D]. 武汉: 华中科技大学, 2015: 9–13.

    WANG W. Research and implementation of battle damage assessment system based on image understanding [D]. Wuhan: Huazhong University of Science and Technology, 2015: 9–13.
    [6]
    勾涛. 基于图像分析的毁伤评估系统关键技术研究 [D]. 长春: 吉林大学, 2019: 5–6.

    GOU T. Research on key technologies of damage assessment system based on image analysis [D]. Changchun: Jilin University, 2019: 5–6.
    [7]
    孔祥锡, 秦闻远, 苏飘逸, 等. 基于深度学习及模糊层次分析的毁伤评估算法 [J]. 航空学报, 2023, 40: 1–18.

    KONG X X, QIN W Y, SU P Y, et al. Damage assessment algorithm based on deep learning and fuzzy analytic hierarchy process [J]. Acta Aeronautica et Astronautica Sinica, 2023, 40: 1–18.
    [8]
    中国人民解放军总装备部军事训练教材编辑工作委员会. 核爆炸物理概论 [M]. 北京: 国防工业出版社, 2003: 51–55.
    [9]
    李秦超, 姚成宝, 程帅, 等. 神经网络状态方程在强爆炸冲击波数值模拟中的应用 [J]. 爆炸与冲击, 2023, 43(4): 044202. DOI: 10.11883/bzycj-2022-0222.

    LI Q C, YAO C B, CHENG S, et al. Application of the neural network equation of state in numerical simulation of intense blast wave [J]. Explosion and Shock Waves, 2023, 43(4): 044202. DOI: 10.11883/bzycj-2022-0222.
    [10]
    GLASSTONE S, DOLAN P J. The effects of nuclear weapons [R]. USA: Defense Technical Information Center, 1977: 453–501. DOI: 10.21236/ada087568.
    [11]
    ZHANG H M, WANG M C, ZHANG Y X, et al. TDA-Net: a novel transfer deep attention network for rapid response to building damage discovery [J]. Remote Sensing, 2022, 14(15): 3687. DOI: 10.3390/rs14153687.
    [12]
    孙家抦. 遥感原理与应用 [M]. 3版. 武汉: 武汉大学出版社, 2013: 174–176.

    SUN J B. Principles and applications of remote sensing [M]. 3rd ed. Wuhan: Wuhan University Press, 2013: 174–176.
    [13]
    全国地震标准化技术委员会. 中华人民共和国地震行业标准: DB/T 75—2018 [S]. 北京: 中国标准出版社, 2019.
    [14]
    卢芳云, 李翔宇, 田占东, 等. 武器毁伤与评估 [M]. 北京: 科学出版社, 2021: 93–152.
  • Relative Articles

    [1]PENG Jiangzhou, PAN Liujuan, GAO Guangfa, WANG Zhiqiao, HU Jie, WU Weitao, WANG Mingyang, HE Yong. Digital intelligence simulation model and application of urban building explosion power field and damage effect[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0471
    [2]LYU Jinxian, WU Hao, LU Yonggang, CHEN De. High-efficiency assessment method of damage for building structures under explosions[J]. Explosion And Shock Waves, 2025, 45(1): 015101. doi: 10.11883/bzycj-2024-0053
    [3]YAO Xiongliang, ZHOU Yanpei, WANG Zhi, WEI Qingyuan. Critical condition for tensile tearing failure of unidirectional stiffened plate under strong impact load[J]. Explosion And Shock Waves, 2024, 44(2): 023104. doi: 10.11883/bzycj-2023-0182
    [4]XIA Mengtao, LI Minghong, ZONG Zhouhong, GAN Lu, HUANG Jie, LI Zhuo. Failure modes of precast segmental concrete-filled double-skin steel tube columns under large equivalent explosion[J]. Explosion And Shock Waves, 2023, 43(11): 112202. doi: 10.11883/bzycj-2022-0385
    [5]XU Tianhan, LI Jie, WANG Mingyang, XU Xiaohui, HE Wenjing. Analysis of test data of underground nuclear explosions and calculation of irreversible deformation range[J]. Explosion And Shock Waves, 2019, 39(12): 121101. doi: 10.11883/bzycj-2018-0505
    [6]XU Xiaohui, QIU Yanyu, WANG Mingyang, SHAO Luzhong. Development of the testing apparatus for modeling large equivalent underground cratering explosions[J]. Explosion And Shock Waves, 2018, 38(6): 1333-1343. doi: 10.11883/bzycj-2017-0144
    [7]GAO Kanghua, ZHAO Tianhui, SUN Song, GUO Qiang. Simplified calculation methods of gaseous explosion effects in buildings[J]. Explosion And Shock Waves, 2018, 38(2): 443-454. doi: 10.11883/bzycj-2016-0201
    [8]Gong Min, Wu Hao-jun, Meng Xiang-dong, Li Yong-qiang. A precisely-controlled blasting method and vibration analysis for tunnel excavation under dense buildings[J]. Explosion And Shock Waves, 2015, 35(3): 350-358. doi: 10.11883/1001-1455(2015)03-0350-09
    [9]ZHONG Guo-sheng, FANG Ying-guang, GU Ren-guo, ZHAO Kui. Safety assessment of structures by blasting vibration based on wavelet analysis[J]. Explosion And Shock Waves, 2009, 29(1): 35-40. doi: 10.11883/1001-1455(2009)01-0035-06
    [10]YANG Guo-liang, YANG Jun, JIANG Lin-lin. Numerical simulations on fold blasting demolition of frame-tube structures[J]. Explosion And Shock Waves, 2009, 29(4): 380-384. doi: 10.11883/1001-1455(2009)04-0380-05
  • Cited by

    Periodical cited type(1)

    1. 庞凯敏. 基于遥感图像中鲁棒交互式提取建筑物的进度研究. 四川建材. 2024(08): 73-75 .

    Other cited types(1)

  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(4)

    Article Metrics

    Article views (376) PDF downloads(153) Cited by(2)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return