基于CRITIC-CW法的地下矿岩体质量评价

doi:10.11872/j.issn.1005-2518.2020.02.030

Abstract

Abstract:

The rock mass of underground mine is a very complex dynamic system，which has many influencing factors.The rock mass quality evaluation is not only an important means to understand the characteristics of underground mine rock mass，but also an important basic data of underground mine design，construction and disaster prevention.The fuzzy and uncertainty of rock mass quality evaluation are strong.Cloud model theory can analyze the fuzzy and quantitative problems，which is very suitable for the random and fuzzy evaluation of multi indexes of rock mass quality evaluation.In order to evaluate the rock mass quality of underground mine more accurately and efficiently，the CRITIC-CW method was proposed，which can quantitatively analyze the fuzziness among many factors affecting the rock mass quality.According to the characteristics of many influencing factors of rock mass quality，rock quality index （RQD），rock uniaxial saturated compressive strength（R_W），rock mass integrity coefficient（Kv），structural plane strength coefficient（K_f） and groundwater seepage amount（ω） were selected to evaluate the quality of underground mine and rock mass.20 groups of sample data of rock mass quality evaluation at home and abroad were collected.The original data were standardized，the standard deviation，the correlation coefficient and the amount of information were calculated by using critical method to quantify the discreteness and internal relationship of sample data，and then the weight of each evaluation index was obtained.Based on the CRITIC-CW method，20 groups of rock mass quality evaluation samples were evaluated，and only one case is misjudged，which shows that the CRITIC-CW method has high accuracy and reliability.Xinli mining area of Sanshandao gold mine is the only gold mine under the sea in China.The geological conditions of the mining area and the quality of surrounding rock are complex.In the production process，local pumice falling，roof falling，collapse and other disasters often occur due to blasting vibration，mechanical drilling and other activities，that seriously threatening the safety of mine production.In order to understand the rock mass quality of the mining area better，the CRITIC-CW method was used to evaluate the rock mass quality of some stopes in Xinli mining area of Sanshandao gold mine.The results show that the rock mass quality of the evaluated stopes is mainly class Ⅲ and class Ⅳ，and the rock mass quality is general.According to the results of the rock mass quality evaluation，after strengthening the support for the stopes and surrounding works with the evaluation class Ⅳ，the caving phenomenon of the mining area is obvious decrease.

Key words: rock mass quality evaluation, CRITIC, Cloud Model （CW）, underground mine, degree of certainty, Sanshandao gold mine

CLC Number:

Wei QI,Wei LI,Zhenyang LI,Guoyan ZHAO. Rock Mass Quality Evaluation of Underground Mine Based on CRITIC-CW Method[J].Gold Science and Technology, 2020, 28(2): 264-270.

Figures/Tables 6

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References 26

1	文畅平. 基于属性数学理论的岩体质量分级方法[J].水力发电学报，2008，27（3）：75-80.
	Wen Changping.Classification of rock-mass stability based on attributive mathematical theory[J].Journal of Hydroelectric Engineering，2008，27（3）：75-80.
2	赵星光，蔡明，蔡美峰.剪胀对地下工程岩体位移的影响——以加拿大 Donkin-Morien隧道为例[J].岩石力学与工程学报，2010，29（11）：2186-2195.
	Zhao Xingguang，Cai Ming，Cai Meifeng.Influence of dilation on rock mass displacement around underground excavations:A case study of Donkin-Morien tunnel in Canada[J].Chinese Journal of Rock Mechanics and Engineering，2010，29（11）：2185-2195.
3	Cai M，Kaiser P.Visualization of rock mass classification systems［J］.Geotechnical and Geological Engineering，2006，24（4）：1089-1102.
4	Barton N，Lien R，Lunde J.Engineering classification of rock masses for the design of tunnel support[J].Rock Mechanics，1974，6（4）：189-236.
5	Bieniawski Z T.Engineering classification of jointed rock masses[J].Transactions of South African Institution of Civil Engineers，1973，15（12）：335-344.
6	邬爱清，汪斌.基于岩体质量指标BQ的岩质边坡工程岩体分级方法[J].岩石力学与工程学报，2014，33（4）：699-706.
	Wu Aiqing，Wang Bin.Engineering rock mass classification method based on rock mass quality index BQ for rock slope[J].Chinese Journal of Rock Mechanics and Engineering，2014，33（4）：699-706.
7	陈昌彦，王贵荣.各类岩体质量评价方法的相关性探讨[J].岩石力学与工程学报，2002，21（12）：1894-1900.
	Chen Changyan，Wang Guirong.Discussion on the interrelation of various rock mass quality classification systems at home and abroad[J].Chinese Journal of Rock Mechanics and Engineering，2002，21（12）：1894-1900.
8	许梦国，王明旭，王平，等.程潮铁矿岩体分级及其在巷道支护中的应用[J].黄金，2013，34（4）：41-45.
	Xu Mengguo，Wang Mingxu，Wang Ping，et al.Rock mass classification and its application in tunnel support in Chengchao iron mine[J].Gold，2013，34（4）：41-45.
9	穆锡川，李景波，刘杰，等.大柳行金矿床节理裂隙调查与岩体质量评价[J].黄金，2017，38（5）：43-46，53.
	Mu Xichuan，Li Jingbo，Liu Jie，et al.Investigation into the joint fissures and evaluation of rock mass quality in Daliuxing gold deposit[J].Gold，2017，38（5）：43-46，53.
10	Shen Y J，Yan R X，Yang G S，et al.Comparisons of evaluation factors and application effects of the new［BQ］_GSI system with international rock mass classification systems[J].Geotechnical and Geological Engineering，2017，35（6）：2523-2548.
11	刘强，李夕兵，梁伟章.岩体质量分类的PCA-RF模型及应用[J].黄金科学技术，2018，26（1）：49-55.
	Liu Qiang，Li Xibing，Liang Weizhang.PCA-RF model for the classification of rock mass quality and its application[J].Gold Science and Technology，2018，26（1）：49-55.
12	张海磊，严文炳，郭生茂，等.基于ELM模型的岩体质量分级及应用[J].黄金，2018，39（12）：32-34.
	Zhang Hailei，Yan Wenbing，Guo Shengmao，et al.Classification of rock mass quality in a mine based on ELM model and its application[J].Gold，2018，39（12）：32-34.
13	邱道宏，陈剑平，阙金声，等.基于粗糙集和人工神经网络的洞室岩体质量评价[J].吉林大学学报（地球科学版），2008，38（1）：86-91.
	Qiu Daohong，Chen Jianping，Que Jinsheng，et al.Evaluation of tunnel rock quality with routh sets theory and artificial neural networks[J].Journal of Jilin University（Earth Science），2008，38（1）：86-91.
14	胡建华，艾自华.基于最优组合赋权的地下矿山岩体质量可拓评价模型［J］.黄金科学技术，2017，25（4）：39-45.
	Hu Jianhua，Ai Zihua.Extension evaluation model of rock mass quality for underground mine based on optimal combination weighting［J］.Gold Science and Technology，2017，25（4）：39-45.
15	吴肖坤，刘敦文，江帆，等.基于特征值域的可拓学理论的工程岩体质量评价［J］.黄金科学技术，2015，23（2）：68-75.
	Wu Xiaokun，Liu Dunwen，Jiang Fan，et al.Extension theory of engineering rock mass quality evaluation based on the feature range［J］.Gold Science and Technology，2015，23（2）：68-75.
16	宫凤强，李夕兵.距离判别分析法在岩体质量等级分类中的应用[J].岩石力学与工程学报，2007，26（1）：190-194.
	Gong Fengqiang，Li Xibing.Application of distance discriminant analysis method to classification of engineering quality of rock masses[J].Chinese Journal of Rock Mechanics and Engineering，2007，26（1）：190-194.
17	梁桂兰，徐卫亚，谈小龙.基于熵权的可拓理论在岩体质量评价中的应用[J].岩土力学，2010，31（2）：535-540.
	Liang Guilan，Xu Weiya，Tan Xiaolong.Application of extension theory based on entropy weight to rock quality evaluation[J].Rock and Soil Mechanics，2010，31（2）：535-540.
18	陈赞成，余斌，胡建军，等.呼的合铜矿工程地质调查与岩体质量评价[J].黄金，2013，34（6）：35-39.
	Chen Zancheng，Yu Bin，Hu Jianjun，et al.Engineering geological investigation and rock mass quality evaluation of Hudehe copper mine[J].Gold，2013，34（6）：35-39.
19	McEwen T，Kapyaho A，Hella P，et al.Rock suitability classification RSC 2012[R].Helsinki：Posiva Oy，2012.
20	李德毅，杜鹢.不确定性人工智能[M].北京：国防工业出版社，2005.
	Li Deyi，Du Yu.Artificial Intelligence with Uncertainty [M].Beijing：National Defense Industry Press，2005.
21	Diakoulaki D，Mavrotas G，Papayannakis L.Determining objective weights in multiple criteria problems：The CRITIC method[J].Computers and Operations Research，1995，22（7）：763-770.
22	李绍红，王少阳，朱建东，等.基于权重融合和云模型的岩爆倾向性预测研究［J］.岩土工程学报，2018，40（6）：1075-1083.
	Li Shaohong，Wang Shaoyang，Zhu Jiandong，et al.Prediction of rock burst tendency based on weighted fusion and improved cloud model［J］.Chinese Journal of Geotechnical Engineering，2018，40（6）：1075-1083.
23	赵国彦，梁伟章，洪昌寿.采空区稳定性的改进云模型二维评判［J］.中国安全科学学报，2015，25（10）：102-108.
	Zhao Guoyan，Liang Weizhang，Hong Changshou. Improved cloud model for two dimensional stability evaluation of goaf［J］.China Safety Science Journal，2015，25（10）：102-108.
24	胡建华，尚俊龙，雷涛.基于RS-TOPSIS法的地下工程岩体质量评价[J].中南大学学报（自然科学版），2012，43（11）：4412-4419.
	Hu Jianhua，Shang Junlong，Lei Tao.Rock mass quality evaluation of underground engineering based on RS-TOPSIS method［J］.Journal of Central South University（Science and Technology），2012，43（11）：4412-4419.
25	蔡广奎.围岩稳定性分类的BP 网络模型的研究[D].南京：河海大学，2001.
	Cai Guangkui.Study of the BP Neural Network on the Stability Classification of Surrounding Rocks[D].Nanjing：Hehai University，2001.
26	杨朝晖，刘浩吾.地下工程围岩稳定性分类的人工神经网络模型[J].四川联合大学学报（工程科学版），1999，3（4）：66-72.
	Yang Chaohui，Liu Haowu.Artificial neural network model for the stability classification of adjoining rock of underground construction[J].Journal of Sichuan Union University（Engineering Science Edition），1999，3（4）：66-72.

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类别	RQD/％	R_W/MPa	K_V	K_f	ω/［L·（min·10 m）^-1］
Ⅰ	90~100	200~120	1.00~0.75	1.0~0.8	0~5
Ⅱ	75~90	120~60	0.75~0.45	0.8~0.6	5~10
Ⅲ	50~75	60~30	0.45~0.30	0.6~0.4	10~25
Ⅳ	25~50	30~15	0.30~0.20	0.4~0.2	25~125
Ⅴ	0~25	15~0	0.20~0.00	0.2~0.0	125~300

样本	评价指标					实测等级	本文结果
样本	X₁	X₂	X₃	X₄	X₅	实测等级	本文结果
1	71.8	90.1	0.57	0.45	0.0	Ⅱ	Ⅱ
2	51.0	40.2	0.38	0.55	10.5	Ⅲ	Ⅲ
3	52.0	25.0	0.22	0.52	12.0	Ⅲ	Ⅲ
4	68.0	90.0	0.38	0.38	21.0	Ⅲ	Ⅲ
5	86.3	105.0	0.68	0.75	6.3	Ⅱ	Ⅱ
6	78.8	75.0	0.53	0.65	8.8	Ⅱ	Ⅱ
7	68.8	52.5	0.41	0.55	13.8	Ⅲ	Ⅲ
8	87.0	95.0	0.70	0.50	9.8	Ⅱ	Ⅱ
9	76.0	90.0	0.57	0.50	11.0	Ⅱ	Ⅱ
10	50.0	35.0	0.30	0.35	20.0	Ⅲ	Ⅲ
11	68.0	90.0	0.57	0.35	18.5	Ⅲ	Ⅲ
12	82.0	95.0	0.70	0.35	0.0	Ⅱ	Ⅱ
13	75.0	87.3	0.30	0.63	0.0	Ⅱ	Ⅱ
14	56.3	37.5	0.34	0.45	21.3	Ⅲ	Ⅲ
15	43.8	26.3	0.28	0.35	50.6	Ⅳ	Ⅳ
16	31.3	18.8	0.23	0.25	100.0	Ⅳ	Ⅳ
17	52.5	28.6	0.38	0.16	23.0	Ⅳ	Ⅲ
18	100.0	200.0	1.00	1.00	0.0	Ⅰ	Ⅰ
19	97.5	180.0	0.94	0.95	1.3	Ⅰ	Ⅰ
20	95.0	160.0	0.88	0.95	2.5	Ⅰ	Ⅰ

样本位置	评价指标
样本位置	X₁	X₂	X₃	X₄	X₅
-200 m中段1#采场	82	81	0.40	0.48	22.0
-200 m中段2#采场	80	81	0.40	0.45	17.0
-240 m中段5#采场	78	80	0.43	0.42	75.0
-320 m中段2#采场	55	82	0.43	0.43	62.5
-320 m中段3#采场	75	78	0.40	0.42	17.5

样本位置	最高确定度	评价结果
-200 m中段1#采场	0.6333	Ⅲ
-200 m中段2#采场	0.7067	Ⅲ
-240 m中段5#采场	0.4437	Ⅳ
-320 m中段2#采场	0.4578	Ⅳ
-320 m中段3#采场	0.7161	Ⅲ

Rock Mass Quality Evaluation of Underground Mine Based on CRITIC-CW Method

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