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Chamber roof deformation prediction and analysis of underground mining using experimental design methodologies

Author

Listed:
  • Zongguo Zhang

    (Central South University)

  • Xianyang Qiu

    (Central South University)

  • Xiuzhi Shi

    (Central South University)

  • Zhi Yu

    (Central South University)

Abstract

The deformation prediction of the chamber roof is crucial in underground mining. Combined with Flac3D numerical simulation, the experimental design methodologies, including single-factor test (SFT), Plackett–Burman design (PBD), steepest ascent design, and response surface methodology, were used to evaluate the effect of multiple variables on the chamber roof deformation. Firstly, eight factors that affected the vertical displacement (Ds) of the chamber roof were selected, and the sensitive interval of every factor was obtained through SFT. Then, four factors that significantly affect the results were screened by PBD: cohesion (Co), stope length (Ls), stope width (Ws), and internal friction angle (fr). Twenty-nine groups of response surface schemes with 4 factors and 3 levels satisfying the Box–Behnken design (BBD) were simulated. Through the result analysis of variance (ANOVA) and sensitivity, the influence order of each factor on Ds can be determined: Ws >Ls >fr > Co > interaction between Co and fr > interaction between Ls and Ws. Finally, using the prediction model, the roof deformation of 0#N stope of a lead–zinc mine was predicted and the error was analyzed. The relative errors between the prediction value and the numerical simulation value, the measured value are 0.7% and 4.3%, respectively, which indicates that the prediction model is reasonable and has a certain reference value for mine safety.

Suggested Citation

  • Zongguo Zhang & Xianyang Qiu & Xiuzhi Shi & Zhi Yu, 2023. "Chamber roof deformation prediction and analysis of underground mining using experimental design methodologies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 757-777, January.
    • Handle: RePEc:spr:nathaz:v:115:y:2023:i:1:d:10.1007_s11069-022-05573-8
    DOI: 10.1007/s11069-022-05573-8
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    References listed on IDEAS

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    1. Jian Zhou & Xibing Li & Hani Mitri, 2015. "Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(1), pages 291-316, October.
    2. Chongchong Qi & Andy Fourie & Xuhao Du & Xiaolin Tang, 2018. "Prediction of open stope hangingwall stability using random forests," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 1179-1197, June.
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