Risk assessment of surface subsidence in karst tunnels under attribute recognition theory

doi:10.16265/j.cnki.issn1003-3033.2022.11.1867

Abstract

Abstract:

In order to accurately predict the risk of surface collapse caused by karst tunnel construction, the risk evaluation of surface collapse along karst tunnels is established by using attribute evaluation method. The key factors of controlling surface collapse along karst tunnels are selected as the evaluation index of tunnel surface collapse risk. The attribute measure functions are constructed to calculate theattribute measures of each risk evaluation index. Using the integrated weighting method to determine the weights of each evaluation index. Finally, a confidence criterion was applied to recognize the surface collapse risk level, and its feasibility is verified by engineering application. The research results indicate that the risk assessment results of ground collapse obtained by the extension risk evaluation system established in this research are in good agreement with the actual engineering practice. In the established risk assessment system, the form of attribute measure function is fixed, and the determination of index weightconsiders the objective cognition of people and objective information of things, which can significantly improve the reliability od risk assessment results.

Key words: attribute recognition, karst tunnels, ground subsidence, risk assessment, comprehensive weights

ZHOU Zelin, ZHANG Kai, ZHANG Heng, CHEN Shougen, GAN Hailong. Risk assessment of surface subsidence in karst tunnels under attribute recognition theory[J]. China Safety Science Journal, 2022, 32(11): 105-112.

Figures/Tables 12

Tab.1

Single index classification standards[2]

指标	属性等级
指标	C₁	C₂	…	$C m - 1$	C_m
I₁	a₁₀-a₁₁	a₁₁-a₁₂	…	$a 1 (m - 2)$ - $a 1 (m - 1)$	>或(<) $a 1 (m - 1)$
I₂	a₂₀-a₂₁	a₂₁-a₂₂	…	$a 2 (m - 2)$ - $a 2 (m - 1)$	>或(<) a_2(m-1)
︙	︙	︙	…	︙	︙
I_n	$a n 0$ - $a n 1$	$a n 1$ - $a n 2$	…	$a n (m - 2)$ - $a n (m - 1)$	>或(<) $a n (m - 1)$

Tab.1

Fig.1

Tab.2

Fig.2

Tab.3

Tab.4

Tab.5

Tab.6

Tab.7

Tab.8

Tab.9

Fig.3

References 20

[1]	刘常军, 傅鹤林, 翟筱松, 等. 铁路超长隧道安全风险管控及隐患排查治理监管[J]. 中国安全科学学报, 2020, 30(增1): 21-26.
	LIU Changjun, FU Helin, ZHAI Xiaosong, et al. Investigation of safety risk control and hidden danger and management supervision of ultra-long railway tunnels[J]. China Safety Science Journal, 2020, 30(S1):21-26. doi: 10.16265/j.cnki.issn1003-3033.2020.S1.005
[2]	张凯. 隐伏岩溶对隧道矿山法施工安全的影响研究[D]. 成都: 西南交通大学, 2019.
	ZHANG Kai. Study on the influence of hidden karst on the safety of tunnel construction by mining method[D]. Chengdu: Southwest Jiaotong University, 2019.
[3]	李术才, 许振浩, 黄鑫, 等. 隧道突水突泥致灾构造分类、地质判识、孕灾模式与典型案例分析[J]. 岩石力学与工程学报, 2018, 37(5):1041-1069.
	LI Shucai, XU Zhenhao, HUANG Xin, et al. Classification, geological identification, hazard mode and typical case studies of hazard-causing structures for water and mud inrush in tunnels[J]. Chinese Journal of Rock Mechanics and Engineering. 2018, 37(5):1041-1069.
[4]	DAY M. An assessment of karstic collapse hazards at Mount Rosser, Ewarton, Jamacia[J]. America Society Civil Engineers, 2003, 122:40-49.
[5]	ZISMAN E. A standard method for sinkhole detection in the Tampa, Florida area[J]. Environmental and Engineering Geoscience, 2001, 7:31-55. doi: 10.2113/gseegeosci.7.1.31
[6]	KATARINA D, DENIE Dr, BARRY K, et al. P Predicting sinkhole susceptibility in Frederick Valley, Maryland, using geographically weighted regression[C]. The 11^th Sinkholes and the Engineering and Environmental Impacts of Karst, 2008, 11:243-255.
[7]	缪钟灵, 宗凤书. 桂林岩溶塌陷风险评价[J]. 中国地质灾害与防治学报, 1995, 6(2):58-66.
	MIAO Zhongling, ZONG Fengshu. On the risk assessment of karst collapse in guiling district[J]. The Chinese Journal of Geological Hazard and Control, 1995, 6(2):58-66.
[8]	张杰, 毕攀, 魏爱华, 等. 基于模糊综合法的烟台市栖霞中桥岩溶塌陷易发性评价[J]. 中国岩溶, 2021, 40(2):215-220.
	ZHANG Jie, BI Pan, WEI Aihua, et al. Assessment of susceptibility to karst collapse in the Qixia Zhongqiao district of Yantai based on fuzzy comprehensive method[J]. Carsologica Sinica, 2021, 40(2):215-220.
[9]	陈学军, 杨越, 白汉营, 等. 基于ANP-模糊聚类分析法的岩溶塌陷研究[J]. 工程地质学报, 2017, 25(5):1213-1219.
	CHEN Xuejun, YANG Yue, BAI Hanying, et al. Use of analytic network process and fuzzy clustering analysis method in karst collapse[J]. Journal of Engineering Geology, 2017, 25(5):1213-1219.
[10]	李忠, 张耀文, 李海君. 改进型BP-网络的岩溶塌陷预测评价[J]. 辽宁工程技术大学学报:自然科学版, 2013, 32(1): 1-6.
	LI Zhong, ZHANG Yaowen, LI Haijun. Prediction evaluation of Karst collapse based on the improved BP-network[J]. Journal of Liaoning Technical University:Natural Science, 2013, 32(1):1-6.
[11]	魏爱华, 马凤山, 邓清海, 等. 广东省某隧道区岩溶塌陷危险性评价[J]. 中国地质灾害与防治学报, 2011, 22(1): 57-62.
	WEI Aihua, MA Fengshan, DENG Qinghai, et al. Evaluation of karst collapse risks along tunnels[J]. The Chinese Journal of Geological Hazard and Control, 2011, 22(1):57-62.
[12]	贺怀振, 魏永耀, 黄敬军. 基于模糊综合评判模型的徐州地铁沿线岩溶塌陷稳定性评价[J]. 中国地质灾害与防治学报, 2017, 28(3):66-72.
	HE Huaizhen, WEI Yongyao, HUANG Jingjun. Stability assessment for karst collapse along Xuzhou Metro using a comprehensive fuzzy model[J]. The Chinese Journal of Geological Hazard and Control, 2017, 28(3):66-72.
[13]	程乾生. 属性识别理论模型及其应用[J]. 北京大学学报:自然科学版, 1997, 33(1):12-20.
	CHENG Qiansheng. Attribute recognition theoretical model with application[J]. Acta Scientiarum Naturalium, 1997, 33(1): 12-20.
[14]	李术才, 周宗青, 李利平, 等. 岩溶隧道突水风险评价理论与方法及工程应用[J]. 岩石力学与工程学报, 2013, 32(9):1858-1867.
	LI Shucai, ZHOU Zongqing, LI Liping, et al. Risk evaluation theory and method of water inrush in karst tunnels and its applications[J]. Chinese Journal of Rock Mechanics and Engineering, 2013, 32(9):1858-1867.
[15]	吴亚楠, 王延岭, 周绍智, 等. 基于综合指数法的泰莱盆地岩溶塌陷风险性评价[J]. 中国岩溶, 2020, 39(3):391-399.
	WU Yan'an, WANG Yanling, ZHOU Shaozhi, et al. Risk assessment of karst collapse in the Tailai basin based on the synthetic index method[J]. Carsologica Sinica, 2020, 39(3):391-399.
[16]	ZHANG Kai, TANNANT D D, ZHENG Wenbo, et al. Prediction of karst for tunnelling using fuzzy assessment combined with geological investigations[J]. Tunnelling and Underground Space Technology, 2018, 80:64-77. doi: 10.1016/j.tust.2018.06.009
[17]	CHANG Dayong. Applications of the extent analysis method on fuzzy AHP[J]. European Journal of Operational Research, 1996, 95(3):649-655. doi: 10.1016/0377-2217(95)00300-2
[18]	张凯, 陈寿根, 霍晓龙, 等. 岩溶地区隧道涌水风险的可拓评价模型及应用[J]. 现代隧道技术, 2019, 56(4):89-96.
	ZHANG Kai, CHEN Shougen, HUO Xiaolong, et al. Extension assessment model for the risk of water inflow in karst tunnels and its application[J]. Modern Tunnelling Technology, 2019, 56(4):89-96.
[19]	NEZARAT H, SERESHKI F, ATAEI M. Ranking of geological risks in mechanized tunneling by using fuzzy analytical hierarchy process (FAHP)[J]. Tunneling and Underground Technology, 2015, 50:358-364.
[20]	ZHANG Kai, ZHENG Wenbo, XU Cong, et al. An improved extension system for assessing risk of water inrush in tunnels in carbonate karst terrain[J]. KSCE Journal of Civil Engineering, 2019, 23(5):2049-2064. doi: 10.1007/s12205-019-0756-0

指标	岩溶塌陷风险等级
指标	C₁	C₂	C₃	C₄
B₁	0~10	10~30	30~50	50~100
B₂	0~0.042	0.042~0.104	0.104~0.254	0.104~0.636
B₃	20~50	10~20	5~10	0~5
B₄	0~25	25~50	50~75	75~100
B₅	0~25	25~50	50~75	75~100
B₆	10~50	5~10	2.5~5	0~2.5
B₇	0~3	3~5	5~10	10~50
B₈	0~25	25~50	50~75	75~100
B₉	50~100	30~50	10~30	0~30
B₁₀	0~2	2~5	5~10	10~50

指标		A₁	A₂		A₃		A₄		A₅		权重
A₁		(1,1,1)	(1,3,5)		(1,3,5)		(3,5,7)		(1,3,5)		0.312
指标	A₁			A₂		A₃		A₄		A₅		权重
A₂	(1/5,1/3,1)			(1,1,1)		(1,2,4)		(1,3,5)		(1,1,1)		0.220
A₃	(1/5,1/3,1)			(1/4,1/2,1)		(1,1,1)		(1,3,5)		(1/4,1/2,1)		0.177
A₄	(1/7,1/5,1/3)			(1/5,1/3,1)		(1,3,5)		(1,1,1)		(1/5,1/3,1)		0.072
A₅	(1/5,1/3,1)			(1,1,1)		(1,2,4)		(1,3,5)		(1,1,1)		0.220

指标	B₁	B₂	权重
B₁	(1,1,1)	(1,2,4)	0.668
B₂	(1/4,1/2,1)	(1,1,1)	0.332
	B₃	B₄	—
B₃	(1,1,1)	(1,1,1)	0.500
B₄	(1,1,1)	(1,1,1)	0.500
	B₅	B₆	—
B₅	(1,1,1)	(1,1,1)	0.500
B₆	(1,1,1)	(1,1,1)	0.500
	B₇	B₈	—
B₇	(1,1,1)	(1,2,4)	0.668
B₈	(1/4,1/2,1)	(1,1,1)	0.332
	B₉	B₁₀	—
B₉	(1,1,1)	(1/4,1/2,1)	0.332
B₁₀	(1,2,4)	(1,1,1)	0.668

指标	局部权重					全局权重
	A	A₂	A₃	A₄	A₅
	0.312	0.220	0.177	0.072	0.220
B₁	0.670	—	—	—	—	0.209
B₂	0.332	—	—	—	—	0.104
B₃	—	0.500	—	—	—	0.110
B₄	—	0.500	—	—	—	0.110
B₅	—	—	0.500	—	—	0.088
B₆	—	—	0.500	—	—	0.088
B₇	—	—	—	0.668	—	0.047
B₈	—	—	—	0.332	—	0.024
B₉	—	—	—	—	0.332	0.073
B₁₀	—	—	—	—	0.668	0.147

里程段编号	里程/m		评价指标
里程段编号	里程/m		B₁	B₂	B₃	B₄		B₅		B₆	B₇		B₈		B₉		B₁₀
S1	23 689.0~23 832.5		35.70	0.105	2.40	10		45		2.10	0		35		4.60		5.08
S2	23 832.5~23 874.7		35.70	0.105	2.70	85		45		1.40	0		35		4.30		5.08
S3	23 874.7~23 887.8		35.70	0.105	3.40	10		45		0.90	0		35		4.30		5.08
S4	23 887.8~24 108.8		9.50	0.105	5.20	85		45		3.34	0		35		17.10		5.08
里程段编号		里程/m	评价指标
里程段编号		里程/m	B₁	B₂	B₃		B₄		B₅	B₆		B₇		B₈		B₉	B₁₀
S5		24 108.8~24 195.0	9.50	0.105	6.30		85		45	0.18		0		35		18.53	5.08
S6		24 195.0~24 260.0	9.50	0.105	6.30		85		45	2.77		0		35		19.60	5.08
S7		24 260.0~24 288.0	9.50	0.105	7.90		90		45	7.90		0		35		14.96	5.08
S8		24 288.0~24 366.3	9.50	0.105	11.90		85		45	11.90		0		35		14.51	5.08
S9		24 366.3~24 489.3	9.50	0.105	4.80		30		45	11.90		0		35		11.13	5.08
S10		24 489.3~24 544.6	9.50	0.105	2.80		10		45	0.10		0		35		17.37	5.08
S11		24 544.6~24 607.1	9.50	0.105	0.56		15		45	2.44		0		35		16.65	5.08
S12		24 607.1~24 616.6	17.60	0.105	0.56		15		60	2.44		0		35		16.65	5.08
S13		24 616.6~24 631.2	17.60	0.105	8.78		70		60	5.98		0		35		1.05	5.60
S14		24 631.2~24 656.7	17.60	0.105	6.13		80		60	3.12		0		35		3.13	5.60
S15		24 656.7~24 687.0	17.60	0.105	6.74		90		60	1.73		0		35		5.37	5.60
S16		24 687.0~24 721.2	17.60	0.105	1.46		15		60	4.04		0		35		10.04	5.60