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Metal Mine ›› 2021, Vol. 50 ›› Issue (03): 184-190.

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Risk Assessment of Collapse Disaster in Goaf of Metal Mine Based on Attribute Recognition Theory

JIN Hao,CHEN Yanhao,ZHOU Zongqing,CHENG Shuai,SUN Zizheng, SHANG Chengshun,WANG Chao   

  1. 1. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061 China;2. School of Qilu Transportation,Shandong University, Jinan 250002,China
  • Online:2021-03-15 Published:2021-03-17

Abstract: The risk assessment of goaf collapse disaster is the key to ensure safe production in mining area. Based on the improved attribute mathematics theory,a risk assessment model of goaf collapse disaster was established,and the risk grade of goaf collapse disaster was evaluated. Firstly,geological factors, mechanical properties,goaf parameters and construction factors are selected as the first-class indicators of attribute evaluation. Secondly,using the principle of importance and the difficulty of obtaining,the four primary indicators were divided into 14 secondary indicators. Thirdly,the analytic hierarchy process method was used to assign weights to the attribute evaluation indicators, and analyze the attribute measure. By constructing the attribute measure function of each evaluation indicator, the single-index attribute measure was calculated and the comprehensive attribute measure was improved. Finally ,the numerical simulation was applied to conduct risk assessment of goaf collapse,and the 3D simulation model of goaf and its surrounding rock was established by FLAC3D software,and the distribution law of plastic zone was analyzed. Based on the established risk assessment system and confidence criterion,the field parameters of Gongchangling mining area were verified,and the risk grade C2 of collapse was evaluated,which indicated that the goaf had medium risk,and the evaluation results were in good agreement with the actual field conditions,which verified the reliability of the evaluation model.

Key words: underground mining, goaf collapse, risk assessment, numerical simulation, attribute recognition