Risk assessment of water inrush disasters of karst tunnels based on variable weight-cloud model: A case study of Zhongliangshan tunnel
-
摘要:
针对岩溶隧道涌突水的致险因素的不确定性、复杂性和隧道涌突水风险评价的主观性，以成渝中线中梁山岩溶隧道工程为背景，建立基于正态云模型的隧道涌突水风险评价方法。通过选取地层岩性、地质构造、地表汇水条件、隧道空间位置、地下水循环交替条件作为风险影响因素，构建涌突水风险评估体系；基于正态云模型确定的各影响因子数字特征及变权向量计算综合隶属度，最终判定岩溶隧道涌突水灾害风险等级。结果表明：成渝中线中梁山隧道涌突水灾害为“II级”与“Ⅴ级”之间，涌突水灾害发生可能性大且危害高，与实际开挖结果一致。文章构建的岩溶隧道涌突水灾害风险评估方法，实现了多元决策下的隧道涌突水灾害风险分级客观性，适合岩溶隧道的风险评估，为日后隧道质量控制和寿命评估提供参考。
Abstract:In order to solve the uncertainty and complexity of risk factors and the subjectivity of risk assessment of water inrush disasters in karst tunnels, the risk of water inrush disaster has been scientifically assessed. According to the Zhongliangshan karst tunnel project on the middle route of Chengdu-Chongqing, the study constructed a risk assessment model of water inrush disasters in karst tunnels based on variable weight-cloud model. First of all, referring to the research methods of Wu Xin, Liu Dunwen and others, and consulting professors in the field of geological disasters and engineers from tunnel construction and inspection units, a total of 8 experts determined the grade and classification standard of each influencing factor on water inrush disasters of karst tunnels, and clarified the parameter value of each influencing factor of Zhongliangshan tunnel on the middle route of Chengdu-Chongqing.
In this study, five influencing factors were selected to construct an index system of risk assessment of the water inrush in karst tunnels. These five factors include formation lithology (calcium carbonate content in strata and rock structure), geologic structures (water-conducting fault structure, water-blocking fault structure and fold structure), surface catchment conditions, tunnel spatial locations and alternating conditions of groundwater circulation. In addition, the grading standards of water inrush disasters were determined, and accordingly the disasters were divided into five risk levels, low, mild, moderate, high and highest.
Firstly, the cloud model was used to determine digital characteristics of the risk level of each index. The diagram of membership cloud of each influencing factor was drawn by MATLAB. The single factor membership degree (μj(x)) of each influencing factor was calculated according to parameter values of water inrush disasters in karst tunnels. Secondly, the analytic hierarchy process (AHP) was used to determine the constant weight. In order to avoid the situation that the constant weight does not change with the state value of the index to be evaluated, the punitive variable weight method was used to determine the variable weight vector (W(x)) and the comprehensive membership degree (U). Finally, according to the principle of maximum membership degree, risk levels of water inrush disasters in karst tunnels were calculated, and water inrush disaster situations of 7 sections in Zhongliangshan Tunnel on the middle route of Chengdu-Chongqing were determined. The results show that water inrush disasters in Zhongliangshan tunnel are between level III and level VI, with a high risk. Among them, DK15 + 630-DK15 + 680 and DK16 + 750-DK16 + 78 are the sections with a moderate risk; DK16 + 020-DK16 + 460 are of high risk; DK14 + 720-DK15 + 630, DK15 + 680-DK16 + 020, DK16 + 460-DK16 + 750 and DK16 + 785-DK17 + 380 are the sections with a highest risk. Water inrush disasters of karst tunnels can be attributed to a variety of influencing factors. The parameter value of each influencing factor of the high-risk section is higher than that of the low-risk section, and the risk of water inrush disasters in a transition zone between a karst area and a non-karst area is the highest. With the large porosity, the developed karst, and active groundwater, the soluble rock stratum is a three-medium system of pores, fissures and pipelines, which provides conditions for the occurrence of water inrush disasters and thus increases disaster possibility.
The assessment result is in consistency with the actual situation of water inrush and tunneling. The consistency indicates that the risk assessment index and its system are applicable to water inrush assessments in karst tunnel areas. The cloud model intuitively reflects a fuzzy membership of risk; the variable weight theory constructs an equilibrium function, and each index is weighted according to the specific situation. It is a good solution to the problem of mutual neutralization between the indexes in the risk assessment of water inrush in karst tunnels, which is conducive to observing the change range and relative importance of each index. The risk assessment method of water inrush disasters of karst tunnels constructed in this paper can realize the objectivity of risk classification of water inrush disasters in tunnels from a multiple decision-making perspective, which is applicable to the risk assessment of karst tunnels and provides reference for the tunnel quality control and life assessment in the future.
-
-
表 1 岩溶隧道涌突水灾害风险影响因素及分级标准
Table 1. Influencing factors and classification standards of water inrush disaster in karst tunnels
影响因素 无风险（Ⅰ级） 轻度风险（Ⅱ级） 中度风险（Ⅲ级） 高度风险（Ⅳ级） 最高风险（Ⅴ级） I I1 I11 1~4 4~8 8~12 12~16 16~20 I12 1~4 4~8 8~12 12~16 16~20 I2 1~4 4~7 7~12 12~17 17~20 I3 I31 1~6 6~10 10~14 14~17 17~20 I32 1~4 4~6 6~10 10~14 14~17 I33 20~16 17~14 14~10 10~4 4~1 I4 20~18 18~14 14~8 8~3 3~1 I5 20~16 16~12 12~8 8~4 4~1 
下载: 导出CSV
表 2 岩溶隧道涌突水影响因素正态云模型数字特征
Table 2. Digital characteristics of normal cloud model for influencing factors of water inrush in karst tunnels
影响因素 Ⅰ级 Ⅱ级 Ⅲ级 Ⅳ级 Ⅴ级 （Ex,En,He） （Ex,En,He） （Ex,En,He） （Ex,En,He） （Ex,En,He） I11 (2.5,0.5,0.01) (6,0.66,0.01) (10,0.66,0.01) (14,0.66,0.01) (18,0.66,0.01) I12 (2.5,0.5,0.01) (6,0.66,0.01) (10,0.66,0.01) (14,0.66,0.01) (18,0.66,0.01) I2 (2.5,0.5,0.01) (5.5,0.5,0.01) (9.5,0.83,0.01) (14.5,0.83,0.01) (18.5,0.5,0.01) I31 (3.5,0.83,0.01) (8,0.66,0.01) (12,0.66,0.01) (15.5,0.5,0.01) (18.5,0.5,0.01) I32 (3.5,0.5,0.01) (5,0.33,0.01) (8,0.66,0.01) (12,0.66,0.01) (15.5,0.5,0.01) I33 (18.5,0.5,0.01) (15.5,0.5,0.01) (12,0.66,0.01) (7,1,0.01) (2.5,0.5,0.01) I4 19,0.33,0.01) (16,066,0.01) (11,1,0.01) (5.5,0.83,0.01) (2,0.33,0.01) I5 (18,0.66,0.01) (14,0.66,0.01) (10,0.66,0.01) (6,0.66,0.01) (2.5,0.5,0.01)
下载: 导出CSV
表 3 岩溶隧道涌突水灾害影响因素基本参数
Table 3. Basic parameters of influencing factors of water inrush disasters in karst tunnels
隧道 DK14+720~
DK15+630DK15+630~
DK15+680DK15+680~
DK16+020DK16+020~
DK16+460DK16+460~
DK16+750DK16+750~
DK16+785DK16+785~
DK17+380I11 19 5 19 8 18.5 8 18.5 I12 18 15 18 15 18 15 18 I2 18 8 17 12 17 8 17 I31 16 7 18 5 18 7 15 I32 6 2 2 4 2 4 2 I33 6 12 12 8 5 3 14 I4 14 15 13 15 14 12 18 I5 15 15 15 9 17 15 7
下载: 导出CSV
表 4 岩溶隧道涌突水灾害影响因素参数值归一化结果
Table 4. Normalized parameter values of influencing factors of water inrush disasters in karst tunnels
隧道 DK14+720~
DK15+630DK15+630~
DK15+680DK15+680~
DK16+020DK16+020~
DK16+460DK16+460~
DK16+750DK16+750~
DK16+785DK16+785~
DK17+380I11 0.95 0.21 0.95 0.37 0.92 0.37 0.92 I12 0.89 0.74 0.89 0.74 0.89 0.74 0.89 I2 0.89 0.37 0.84 0.58 0.84 0.37 0.84 I31 0.79 0.32 0.89 0.21 0.89 0.32 0.74 I32 0.31 0.06 0.06 0.19 0.06 0.19 0.06 I33 0.26 0.58 0.58 0.37 0.21 0.11 0.68 I4 0.68 0.74 0.63 0.74 0.68 0.58 0.89 I5 0.74 0.74 0.74 0.42 0.84 0.74 0.32
下载: 导出CSV
表 5 岩溶隧道涌突水灾害风险等级综合隶属度级评估结果
Table 5. Assessment of comprehensive membership grades of water inrush disasters in karst tunnels
隧道 DK14+720~
DK15+630DK15+630~
DK15+680DK15+680~
DK16+020DK16+020~
DK16+460DK16+460~
DK16+750DK16+750~
DK16+785DK16+785~
DK17+380I级 0.000 5 0.010 5 0.007 0 0.092 9 0.041 8 0.059 6 0.013 1 II级 0.104 5 0.225 6 0.098 8 0.091 9 0.062 8 0.174 2 0.070 4 III级 0.012 9 0.122 4 0.086 5 0.059 8 0.006 6 0.098 9 0.007 1 Ⅳ级 0.098 6 0.047 4 0.006 8 0.110 4 0.029 6 0.043 5 0.092 1 Ⅴ级 0.147 2 0.000 5 0.154 7 0.000 4 0.172 0 0.058 7 0.125 6 风险等级 Ⅴ级 II级 Ⅴ级 III级 Ⅴ级 II级 Ⅴ级
下载: 导出CSV
-
[1] 王梦恕. 中国铁路、隧道与地下空间发展概况[J]. 隧道建设, 2010, 30(4):351-364.
WANG Mengshu. An overview of development of railways, tunnels and underground works in China[J]. Tunnel Construction, 2010, 30(4):351-364.
[2] 谢国文, 杨平恒, 卢丙清, 陈峰, 张宇, 池彦宾. 基于高分辨率示踪技术的岩溶隧道涌水来源识别及含水介质研究[J]. 中国岩溶, 2018, 37(6):892-899.
XIE Guowen, YANG Pingheng, LU Bingqing, CHEN Feng, ZHANG Yu, CHI Yanbin. Source identification of karst tunnel gushing water and study of aquifer media based on high-resolution tracer technology[J]. Carsologica Sinica, 2018, 37(6):892-899.
[3] 王家楠, 蒋勇军, 贺秋芳, 范佳鑫, 何瑞亮, 吴超. 中梁山岩溶槽谷区荒草地土壤微生物群落对隧道建设的响应[J]. 生态学报, 2019, 39(16):6136-6145.
WANG Jianan, JIANG Yongjun, HE Qiufang, FAN Jiaxin, HE Ruiliang, WU Chao. Response of soil microbial community in grassland to tunnel construction in the karst trough valley, Zhongliang Mountain, Chongqing[J]. Acta Ecologica Sinica, 2019, 39(16):6136-6145.
[4] 徐颖, 左昌群, 陈志超, 方晓睿. 推覆构造带碳酸盐岩隧道突水机制及风险规避[J]. 岩石力学与工程学报, 2014, 33(Supp.1):2885-2893.
XU Ying, ZUO Changqun, CHEN Zhichao, FANG Xiaorui. Karst water inrush mechanism and risk mitigation of tunnel hosted in carbonate of nappe structure belts[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33(Supp.1):2885-2893.
[5] 魏兴萍, 张虹, 苏程烜. 重庆南山隧道工程涌水隐患研究[J]. 中国岩溶, 2016, 35(1):74-80.
WEI Xingping, ZHANG Hong, SU Chengxuan. Hazard of water gushing caused by Nanshan Tunnel engineering, Chongqing[J]. Carsologica Sinica, 2016, 35(1):74-80.
[6] 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.
[7] Sun Xingliang, Teng Guo, Guo Xiaolong, Li Xinzhi. Risk assessment of water inrush in karst tunnels based on fuzzy comprehensive evaluation considering misjudgment losses: A case study[J]. Arabian Journal of Geosciences, 2022, 15: 421-439.
[8] 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, Hunan Province based on AHP and GIS[J]. Carsologica Sinica, 2022, 41(1):21-33.
[9] 杨卓, 马超. 基于BP神经网络方法的岩溶隧道突涌水风险预测[J]. 隧道建设, 2016, 36(11):1337-1342.
YANG Zhuo, MA Chao. Risk prediction of water inrush in karst tunnels based on BP neural network[J]. Tunnel Construction, 2016, 36(11):1337-1342.
[10] 袁青, 陈培帅, 钟涵, 江鸿, 吴诗琦, 闫鑫雨. 基于优化FAHP-TOPSIS法的高压富水花岗岩断层涌水预测[J]. 隧道建设(中英文), 2019, 39(5):766-774.
YUAN Qing, CHEN Peishuai, ZHONG Han, JIANG Hong, WU Shiqi, YAN Xinyu. Water gushing prediction in high-pressure water-rich granite fault zone based on optimized FAHP-TOPSIS method[J]. Tunnel Construction, 2019, 39(5):766-774.
[11] 黄小城, 陈秋南, 阳跃朋, 张志敏. 可拓理论对复杂条件下岩溶隧道的风险评估[J]. 地下空间与工程学报, 2013, 9(5):1179-1185.
HUANG Xiaocheng, CHEN Qiunan, YANG Yuepeng, ZHANG Zhimin. Risk evaluation of karst tunnel under complex geological condition with extension theory[J]. Chinese Journal of Underground Space and Engineering, 2013, 9(5):1179-1185.
[12] 徐选华, 赵程伟, 何继善, 刘瑞环. 多型异构数据下关联变权空间多属性决策方法[J]. 系统工程理论与实践, 2020, 40(7):1895-1905.
XU Xuanhua, ZHAO Chengwei, HE Jishan, LIU Ruihuan. Association variable weight space multi-attribute decision method under multi-type heterogeneous data[J]. Systems Engineering-Theory & Practice, 2020, 40(7):1895-1905.
[13] Shi Liangliang, Wang Xia, Shen Yongliang. Research on 3D face recognition method based on LBP and SVM[J]. Optik-International Journal for Light and Electron Optics, 2020, 220:165157. doi: 10.1016/j.ijleo.2020.165157
[14] Zhou Ping, Meng Luoming, Qiu Xuesong, Wang Zesheng, Wang Zhipeng, Chen Zhifeng. Evaluation of cloud service reliability based on classified statistics and hierarchy variable weight[J]. Journal of Signal Processing Systems, 2019, 91(10):1115-1126. doi: 10.1007/s11265-018-1407-2
[15] Lei Junjie, Yang Wunian, Yang Xin. Soil moisture in a vegetation-covered area using the improved water cloud model based on remote sensing[J]. Journal of the Indian Society of Remote Sensing, 2021, 50(1):1-11.
[16] 杨洁, 王国胤, 刘群, 郭毅可, 刘悦, 淦文燕, 刘玉超. 正态云模型研究回顾与展望[J]. 计算机学报, 2018, 41(3):724-744. doi: 10.11897/SP.J.1016.2018.00724
YANG Jie, WANG Guoyin, LIU Qun, GUO Yike, LIU Yue, GAN Wenyan, LIU Yuchao. Retrospect and prospect of research of normal cloud model[J]. Chinese Journal of Computers, 2018, 41(3):724-744. doi: 10.11897/SP.J.1016.2018.00724
[17] 孙庆鹏, 李战武, 常一哲. 基于威力势场的多机种威胁评估方法[J]. 系统工程与电子技术, 2018, 40(9):1993-1999.
SUN Qingpeng, LI Zhanwu, CHANG Yizhe. Multi-types airplane threat assessment based on combat power field[J]. Systems Engineering and Electronics, 2018, 40(9):1993-1999.
[18] 张萌, 王俊智, 李洁祥, 张波, 王心义. 基于变权理论与物元可拓模型的矿井水质识别[J]. 环境科学与技术, 2021, 44(Supp.1):66-72.
ZHANG Meng, WANG Junzhi, LI Jiexiang, ZHANG Bo, WANG Xinyi. Mine water quality evaluation based on coupled variable weight theory and improved matter-element extension model[J]. Environmental Science & Technology, 2021, 44(Supp.1):66-72.
[19] 贺华刚. 基于相关性准则和R-ELM模型的岩溶隧道涌水量预测研究[J]. 隧道建设(中英文), 2019, 39(8):1262-1269.
HE Huagang. Prediction of water inflow in karst tunnels based on correlation criterion and R-ELM model[J]. Tunnel Construction, 2019, 39(8):1262-1269.
[20] 李芳涛, 李华明, 胡志平, 陈南南, 晏长根. 峨汉高速廖山隧道岩溶发育规律及其工程效应浅析[J]. 中国岩溶, 2020, 39(4):592-603.
LI Fangtao, LI Huaming, HU Zhiping, CHEN Nannan, YAN Changgen. Features of karst development and geotechnical effects in the Liaoshan Tunnel on the E-Han expressway[J]. Carsologica Sinica, 2020, 39(4):592-603.
[21] 张银松, 曹聪, 康世海, 刘家富. 重庆市中梁山地区隐伏塌陷特征及物探勘测的思路[J]. 中国岩溶, 2020, 39(6):918-927.
ZHANG Yinsong, CAO Cong, KANG Shihai, LIU Jiafu. Characteristics of hidden karst collapse in the Zhongliangshan area of Chongqing and an approach of geophysical surveys[J]. Carsologica Sinica, 2020, 39(6):918-927.
[22] 白雪飞, 崇六喜, 丁国玺. 鄂尔多斯地区铁路地下水路堑处理技术[A]// 中国铁道学会铁道工程学会工程地质与路基专业委员会第二十三届年会论文集 [C]. 西南交通大学学报, 2012:143-146
BAI Xuefei, CHONG Liuxi, DING Guoxi. The treatment technology of railway cutting with underground water in Ordos area[A]//Proceedings of the 23rd Annual Meeting of Engineering Geology and Subgrade Professional Committee of Railway Engineering Society of China Railway Society[C]. Journal of Southwest Jiaotong University, 2012:143-146.
[23] 吴泽, 蒋勇军, 姜光辉, 王正雄, 贺秋芳, 白莹. 中梁山岩溶槽谷区不同土地利用方式坡地产流规律[J]. 生态学报, 2019, 39(16):6072-6082.
WU Ze, JIANG Yongjun, JIANG Guanghui, WANG Zhengxiong, HE Qiufang, BAI Ying. Characteristics of different land-use types of slope runoff in a karst trough valley located in Zhongliang Mountain, Chongqing[J]. Acta Ecologica Sinica, 2019, 39(16):6072-6082.
[24] 周正, 李大华, 廖云平, 林军志, 张烨, 陈洪凯, 祁永爱, 王贺. 重庆中梁山岩溶地面塌陷特征及形成机理[J]. 中国岩溶, 2022, 41(1):67-78.
ZHOU Zheng, LI Dahua, LIAO Yunping, LIN Junzhi, ZHANG Ye, CHEN Hongkai, QI Yongai, WANG He. Characteristics and formation mechanism of karst ground collapse in Zhongliangshan area, Chongqing[J]. Carsologica Sinica, 2022, 41(1):67-78.
[25] 彭旭东, 戴全厚, 李昌兰. 模拟降雨下喀斯特坡耕地土壤养分输出机制[J]. 生态学报, 2018, 38(2):624-634.
PENG Xudong, DAI Quanhou, LI Changlan. Output mechanism of soil nutrients from karst slope farmland under simulated rainfall[J]. Acta Ecologica Sinica, 2018, 38(2):624-634.
[26] 覃自阳, 甘凤玲, 何丙辉. 岩层倾向对喀斯特槽谷区地表/地下产流过程的影响[J]. 水土保持学报, 2020, 34(5):68-75, 80.
QIN Ziyang, GAN Fengling, HE Binghui. Influence of strata tendency on the surface/underground runoff production process in typical karst valley[J]. Journal of Soil and Water Conservation, 2020, 34(5):68-75, 80.
[27] 李强. 重庆中梁山地区近邻隧道建设地下水环境效应研究[D]. 成都: 成都理工大学, 2017.
LI Qiang. Study on the groundwater environmental effects caused by construction of neighboring tunnels in Zhongliang Mountain area of Chongqing[D]. Chengdu: Chengdu University of Technology, 2017.
[28] Peng X D, Dai Q H, Li C L, Zhao L S. Role of underground fissure flow in near-surface rainfall-runoff process on a rock mantled slope in the karst rocky desertification area[J]. Engineering Geology, 2018, 243:10-17. doi: 10.1016/j.enggeo.2018.06.007
[29] 刘敦文, 曹敏, 唐宇, 徐谦, 姜冰, 王方立. 基于云模型的富水岩溶隧道涌水风险评价[J]. 中国安全生产科学技术, 2021, 17(1):109-115.
LIU Dunwen, CAO Min, TANG Yu, XU Qian, JIANG Bing, WANG Fangli. Risk assessment of water inrush in water-rich karst tunnel based on cloud model[J]. Journal of Safety Science and Technology, 2021, 17(1):109-115.
[30] 梁辉如, 王永东, 彭浩, 刘俊锋, 燕新. 基于正态云理论的软弱隧道围岩分级[J]. 重庆交通大学学报(自然科学版), 2021, 40(11):82-87.
LIANG Huiru, WANG Yongdong, PENG Hao, LIU Junfeng, YAN Xin. Classification of soft surrounding rock of tunnel based on normal cloud theory[J]. Journal of Chongqing Jiaotong University (Natural Science), 2021, 40(11):82-87.
[31] 王桂林, 强壮, 曹聪, 陈瑶, 郝晋渝. 基于地理探测器与层次分析法的岩溶地面塌陷易发性评价: 以重庆中梁山地区为例[J]. 中国岩溶, 2022, 41(1):79-87.
WANG Guilin, QIANG Zhuang, CAO Cong, CHEN Yao, HAO Jinyu. Evaluation of susceptibility to karst collapse based on geodetector and analytic hierarchy method: An example of the Zhongliangshan area in Chongqing[J]. Carsologica Sinica, 2022, 41(1):79-87.
[32] 李洪兴. 因素空间理论与知识表示的数学框架(Ⅷ):变权综合原理[J]. 模糊系统与数学, 1995(3):1-9.
LI Hongxing. Factor spaces and mathematical frame of knowledge representation (VIII): Variable weights analysis[J]. Fuzzy Systems and Mathematics, 1995(3):1-9.
[33] 张楚旋, 李夕兵, 董陇军, 姚金蕊. 微震监测传感器布设方案评价模型及应用[J]. 东北大学学报(自然科学版), 2016, 37(4):594-598, 608.
ZHANG Chuxuan, LI Xibing, DONG Longjun, YAO Jinrui. Evaluation model of microseismic monitoring sensor layout scheme and its application[J]. Journal of Northeastern University (Natural Science), 2016, 37(4):594-598, 608.
[34] 康虔, 王新民, 蒲浩, 王石. 基于变权-未确知测度理论的岩溶路基稳定性分析[J]. 东北大学学报(自然科学版), 2016, 37(3):435-439.
KANG Qian, WANG Xinmin, PU Hao, WANG Shi. Analysis of subgrade stability in karst area based on variable weight theory-uncertainty measurement method[J]. Journal of Northeastern University (Natural Science), 2016, 37(3):435-439.
[35] 陈紫云, 陈敏, 代绍述, 蓝香源, 杨善元, 胡聪. 西南某山区高速公路岩溶隧道的涌水灾害危险性研究[J]. 地质灾害与环境保护, 2017, 28(2):60-69.
CHEN Ziyun, CHEN Min, DAI Shaoshu, LAN Xiangyuan, YANG Shanyuan, HU Cong. The study of water-gushing disaster and risk in one hightway tunnel of China western karst–mountainous area[J]. Journal of Geological Hazards and Environment Preservation, 2017, 28(2):60-69.
-
图(4)
表(5)
计量
- 文章访问数: 1402
- PDF下载数: 111
- 施引文献: 0