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Volume 42 Issue 6
Dec.  2023
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Article Navigation >  CARSOLOGICA SINICA  > 2023  >  42(6): 1294-1302
LU Yulong, YE Gaofeng, YANG Xian, LU Zhilin, LIU Yang, ZHANG Lianzhi, LI Ganlong. Study on susceptibility of karst collapse based on normal cloud model in Yonghe town, Liuyang City[J]. CARSOLOGICA SINICA, 2023, 42(6): 1294-1302. doi: 10.11932/karst2023y027
Citation: LU Yulong, YE Gaofeng, YANG Xian, LU Zhilin, LIU Yang, ZHANG Lianzhi, LI Ganlong. Study on susceptibility of karst collapse based on normal cloud model in Yonghe town, Liuyang City[J]. CARSOLOGICA SINICA, 2023, 42(6): 1294-1302. doi: 10.11932/karst2023y027
shu

Study on susceptibility of karst collapse based on normal cloud model in Yonghe town, Liuyang City

doi: 10.11932/karst2023y027
  • 1.

    School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China

  • 2.

    College of Civil Engineering, Hunan City University, Yiyang, Hunan 4130003, China

  • 3.

    The Second Team of Hunan Bureau of Nonferrous Geological Prospecting, Xiangtan, Hunan 411102, China

  • Received Date: 2022-08-08
    Available Online: 2023-12-28
    Abstract
  • Ground collapse is a common geological disaster in karst area, which would bring great harm to people's life and property. The zoning evaluation on karst collapse susceptibility is beneficial to the classification and treatment of the disaster in order to ensure safety and economy. Karst ground collapse is characterized by suddenness, concealment, multi-factor, randomness and fuzziness; therefore, it is difficult to be fully quantified. The normal cloud model could effectively reflect the fuzziness and randomness of objective things, and integrate them to form the mapping between qualitative and quantitative analyses. In this paper, based on the normal cloud model, the study on susceptibility of karst collapse in Yonghe town, Liuyang City has been conducted in order to provide a basis for the classification and treatment of ground collapse in this area, and also provide a reference for the susceptibility evaluation of karst collapse in other areas.The study area is located to the northeast of Liuyang City, 24 km away from it, and its administrative division is located in Yonghe town, Liuyang City, Hunan Province. Ground collapses in this area are all developed in the distribution area of soluble rock layer. The most developed layer is the second member of the Lower Permian Qixia Formation (P1q2) with thick-layered carbonate rocks. Secondarily, ground collapses are distributed in the middle and upper Carboniferous Hutian Group (C2+3ht). The study area covers an area of 14 km2, and 38 karst collapses have occurred in this area so far. Among these collapses, 22 have been surveyed and filled. Sixteen collapses, four of which have been filled, have been investigated in detail. The collapse sites are mainly distributed in the region of soluble rock where faults are developed and surface water and groundwater are closely connected. Geographically, the collapse sites are mainly distributed in Yueshan Formation-Oujia Formation-Dahe Formation, Yongfu village, Yonghe town, Juxiang community-the old street of Yonghe-Huayuan village, Yanxi town, Nanshan Formation-Lizhen Primary School (old)-Xinwan Formation-Xinping Formation.Six evaluation indexes of karst collapse susceptibility were selected in this study, including karst development degree (dissolution rate of borehole), distance from the fault in the area, thickness of overlying soil layer, characteristics of karst water, distance from the pumping funnel center and current situation of ground collapse (ground collapse density). Firstly, according to the detailed exploration results of existing subsidence pits, the weights of six evaluation indicators of 16 existing subsidence pits were calculated and assigned based on entropy weight method. The calculation results indicate that the weight of development density of existing ground collapses is the largest, and that of the fluctuation amplitude of groundwater is the smallest. Secondly, the scoring standard for the risk level of normal cloud model was determined, and the susceptibility of karst collapse in each unit area in the study area was evaluated combined with the weight of each evaluation index. The evaluation results show that all the karst collapses that occurred are distributed in the area highly subject to karst collapse, indicating that the zoning in the evaluation of this study is reasonable.When the normal cloud model is used to evaluate the susceptibility of karst collapse, the size of grid cells could be controlled by the cloud similarity of comprehensive risk evaluation of karst collapse. For areas with high similarity and small cloud droplet dispersion, the unit area of each evaluation could be appropriately increased in order to reduce the evaluation workload. In addition, the high risk area calculated by analytic hierarchy process is 10.8% larger than that by normal cloud method, but three existing collapse pits distributed in the area at medium-level risk of karst collapse, indicating that normal cloud model takes more advantages than analytic hierarchy process in dealing with fuzzy and random problems such as the risk evaluation of karst collapse.

     

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    • References(20)
  • [1]
    项式均, 陈健, 覃有强. 湖北大冶县大广山铁矿岩溶塌陷的预测和评价[J]. 中国岩溶, 1987, 6(4):297-314.

    XIANG Shijun, CHEN Jian, QIN Youqiang. Prediction and evaluation of karst collapse in Daguangshan iron mine in Daye county, Hubei[J]. Carsologica Sinica, 1987, 6(4):297-314.
    [2]
    贾秀梅, 周骏业, 董玉良, 张发旺. 灰色系统理论在岩溶地面塌陷分析预测中的应用[J]. 中国地质灾害与防治学报, 1994, 5(Suppl.1):113-117.

    JIA Xiumei, ZHOU Junye, DONG Yuliang, ZHANG Fawang. Application of the grey system model in the prediction of karst ground collapse[J]. The Chinese Journal of Geological Hazard and Control, 1994, 5(Suppl.1):113-117.
    [3]
    包惠明, 胡长顺. 岩溶地面塌陷神经网络预测[J]. 工程地质学报, 2002, 10(3): 299-304.

    BAO Huiming, HU Changshun. Neural network prediction of karstic ground collapse[J]. Journal of Engineering Geology, 2002, 10(3): 299-304.
    [4]
    陈菊艳, 朱斌, 彭三曦, 单慧媚. 基于AHP和GIS的矿区岩溶塌陷易发性评估:以贵州林歹岩溶矿区为例[J]. 自然灾害学报, 2021, 30(5):226-236.

    CHEN Juyan, ZHU Bin, PENG Sanxi, SHAN Huimei. Assessment of susceptibility to karst collapse in mining area based on AHP and GIS: A case study in Lindai karst mining area in Guizhou[J]. Journal of Natural Disasters, 2021, 30(5):226-236.
    [5]
    武鑫, 黄敬军, 缪世贤. 基于层次分析–模糊综合评价法的徐州市岩溶塌陷易发性评价[J]. 中国岩溶, 2017, 36(6):836-841.

    WU Xin, HUANG Jingjun, MIAO Shixian. Susceptibility zoning and mapping of karst collapse in Xuzhou using analytic hierarchy process-fuzzy comprehensive evaluation method[J]. Carsologica Sinica, 2017, 36(6):836-841.
    [6]
    蒙彦, 雷明堂. 岩溶塌陷研究现状及趋势分析[J]. 中国岩溶, 2019, 38(3):411-417.

    MENG Yan, LEI Mingtang. Analysis of situation and trend of sinkhole collapse[J]. Carsologica Sinica, 2019, 38(3):411-417.
    [7]
    李德毅, 刘常昱. 论正态云模型的普适性[J]. 中国工程科学, 2004, 6(8):28-34. doi: 10.3969/j.issn.1009-1742.2004.08.006

    LI Deyi, LIU Changyu. Study on the universality of the normal cloud model[J]. Engineering Science, 2004, 6(8):28-34. doi: 10.3969/j.issn.1009-1742.2004.08.006
    [8]
    龚艳冰, 巢妍. 基于不确定正态云组合权重的综合评价方法研究[J]. 统计与信息论坛, 2020, 35(5):3-8. doi: 10.3969/j.issn.1007-3116.2020.05.001

    GONG Yanbing, CHAO Yan. Research on comprehensive evaluation method based on uncertain normal cloud combination weight[J]. Statistics & Information Forum, 2020, 35(5):3-8. doi: 10.3969/j.issn.1007-3116.2020.05.001
    [9]
    付学文. 基于云模型的遗传算法的研究[D]. 哈尔滨: 哈尔滨工业大学, 2011.

    FU Xuewen. Research on genetic algorithm based on cloud model[D]. Harbin: Harbin Institute of Technology, 2011.
    [10]
    袁爱平. 基于层次分析法–正态云模型的岩质边坡稳定性预测[J]. 水电能源科学, 2016, 34(9):153-156.

    YUAN Aiping. Stability evaluation of rocky slope based on AHP-normal cloud model[J]. Water Resources and Power, 2016, 34(9):153-156.
    [11]
    李明亮, 李克钢, 刘月东, 吴顺川, 秦庆词, 王航龙. 基于变异系数与序关分析法–多维正态云模型的岩爆预测[J]. 岩石力学与工程学报, 2020, 39(Suppl.2):3395-3402.

    LI Mingliang, LI Kegang, LIU Yuedong, WU Shunchuan, QIN Qingci, WANG Hanglong. Rock burst prediction based on coefficient of variation and sequence analysis-multidimensional normal cloud model[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(Suppl.2):3395-3402.
    [12]
    中国地质灾害防治工程行业协会. 岩溶地面塌陷防治工程勘查规范(试行)T/CAGHP076-2020[S]. 武汉: 中国地质大学出版社, 2020.

    China Association of Geological Hazard Prevention. Code for engineering investigation of karst ground collapse prevention (Trial) T/CAGHP076-2020S]. Wuhan: China University of Geosciences Press, 2020.
    [13]
    李文博, 张洪岩, 彭超. 基于AHP-FCE模型的岩溶地面塌陷灾害易发程度分区评价[J]. 中国矿业, 2021, 30(6):195-199.

    LI Wenbo, ZHANG Hongyan, PENG Chao. Evaluation of the susceptibility of karst ground collapse disaster based on AHP-FCE model[J]. China Mining Magazine, 2021, 30(6):195-199.
    [14]
    刘秀敏, 陈从新, 沈强, 陈建胜. 覆盖型岩溶塌陷的空间预测与评价[J]. 岩土力学, 2011, 32(9):2785-2790. doi: 10.3969/j.issn.1000-7598.2011.09.037

    LIU Xiumin, CHEN Congxin, SHEN Qiang, CHEN Jiansheng. Spatial prediction and evaluation of collapse of covered karst[J]. Rock and Soil Mechanics, 2011, 32(9):2785-2790. doi: 10.3969/j.issn.1000-7598.2011.09.037
    [15]
    姜伏伟, 张发旺, 柳林, 刘伟, 李亮, 陈航. 南宁地铁施工降水诱发岩溶塌陷条件及安全防控措施[J]. 中国岩溶, 2018, 37(3):415-420.

    JIANG Fuwei, ZHANG Fawang, LIU Lin, LIU Wei, LI Liang, CHEN Hang. Dewatering induced karst collapse conditions and safety prevention and control measures in Nanning subway construction[J]. Carsologica Sinica, 2018, 37(3):415-420.
    [16]
    Taheri K, Gutierrez F, Mohseni H, Raeisi E, Taheri M. Sinkhole susceptibility mapping using the Analytical Hierarchy Process (AHP) and magnitude-frequency relationships: A case study in Hamadan Province, Iran[J]. Geomorphology, 2015, 234:64-79. doi: 10.1016/j.geomorph.2015.01.005
    [17]
    吴远斌, 刘之葵, 殷仁朝, 雷明堂, 戴建玲, 罗伟权, 潘宗源. 基于AHP和GIS技术的湖南怀化地区岩溶塌陷易发性评价[J]. 中国岩溶, 2022, 41(1):21-33.

    WU Yuanbin, LIU Zhikui, YIN Renchao, LEI Mingtang, DAI Jianling, LUO Weiquan, PAN Zongyuan. Evaluation of karst collapse susceptibility in Huaihua area, Hunan Province based on AHP and GIS[J]. Carsologica Sinica, 2022, 41(1):21-33.
    [18]
    阮永芬, 张虔, 闫明, 郭宇航, 蔡龙. 基于AHP–信息熵权–模糊集的岩溶塌陷风险评价[J]. 安全与环境学报, 2022, 22(6):2986-2993.

    RUAN Yongfen, ZHANG Qian, YAN Ming, GUO Yuhang, CAI Long. Karst collapse risk assessment based on AHP-EWM-FT[J]. Journal of Safety and Environment, 2022, 22(6):2986-2993.
    [19]
    陈学军, 陈李洁, 宋宇, 毕鹏雁. 熵权–正态云模型岩溶塌陷预测分析[J]. 工程地质学报, 2019, 27(6):1389-1394.

    CHEN Xuejun, CHEN Lijie, SONG Yu, BI Pengyan. Prediction and analysis of karst collapse with entropy-normal cloud model[J]. Journal of Engineering Geology, 2019, 27(6):1389-1394.
    [20]
    杨仙, 张可能, 岳健, 黄常波. 基于熵度量法的盾构施工过程风险评价[J]. 湖南科技大学学报(自然科学版), 2015, 30(4):64-68.

    YANG Xian, ZHANG Keneng, YUE Jian, HUANG Changbo. Risk assessment in shield construction based on entropy theory[J]. Journal of Hunan University of Science & Technology (Natural Science Edition), 2015, 30(4):64-68.
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