Risk assessment of water inrush disasters of karst tunnels based on variable weight-cloud model: A case study of Zhongliangshan tunnel
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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.
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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 
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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)
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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
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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
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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
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