DOI QR코드

DOI QR Code

Recognition and prevention of rockfall vulnerable area in open-pit mines based on slope stability analysis

  • Zhu, Chun (School of Earth Sciences and Engineering, Hohai University) ;
  • He, Manchao (State Key Laboratory for Geomechanics & Deep Underground Engineering) ;
  • Tao, Zhigang (State Key Laboratory for Geomechanics & Deep Underground Engineering) ;
  • Meng, Qingxiang (Research Institute of Geotechnical Engineering, Hohai University) ;
  • Zhang, Xiaohu (School of Civil Engineering, Guizhou University of Engineering Science)
  • 투고 : 2021.04.12
  • 심사 : 2021.08.11
  • 발행 : 2021.09.10

초록

Because of a wide distribution range, sudden occurrence, and high frequency of rockfall disasters on the slope of open-pit mines, it is difficult to effectively control the rockfall disasters in open-pit mines. The slope stabilities of slopes of 13 typical sections in the Changshanhao open-pit mine were calculated using 3DEC software, and the vulnerable area of each slope section was determined. These areas were analyzed as high-incidence areas of rockfalls. Combined with the field geological conditions, the slopes of the W6 and W8 sections where rockfall disasters easily occur were selected to study the motion characteristics of rockfalls, including the trajectory, landing distribution, bouncing height, and total kinetic energy using Rocfall software. According to different distribution characteristics of high-incidence areas of rockfall disasters on a slope, the gravel cushion and protective net methods are proposed to control rockfall disasters. The effectiveness and reasonableness of prevention methods were validated using numerical simulation, proving a good basis for scientific prevention and control of rockfall disasters in open-pit mines.

키워드

과제정보

This work was supported by the National Natural Science Foundation of China (52104125), the Fundamental Research Funds for the Central Universities (No. B210201001) and the Technology top talent support project of Guizhou Provincial Education Department ([2020]155), Research and development project of Guizhou University of Engineering Science (Grant No: G2018016), Bijie city science and technology plan joint fund project ([2019]26).

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