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Journal of Central South University

, Volume 25, Issue 2, pp 406–417 | Cite as

Deformation prediction and analysis of underground mining during stacking of dry gangue in open-pit based on response surface methodology

  • Xian-yang Qiu (邱贤阳)
  • Jia-yao Chen (陈佳耀)
  • Xiu-zhi Shi (史秀志)
  • Shu Zhang (张舒)
  • Jian Zhou (周健)
  • Xin Chen (陈新)
Article
  • 43 Downloads

Abstract

Deformation prediction and the analysis of underground goaf are important to the safe and efficient recovery of residual ore when shifting from open-pit mining to underground mining. To address the comprehensive problem of stability in the double mined-out area of the Tong-Lv-Shan (TLS) mine, which employed the dry stacked gangue technology, this paper applies the function fitting theory and a regression analysis method to screen the sensitive interval of four influencing factors based on single-factor experiments and the numerical simulation software FLAC3D. The influencing factors of the TLS mine consist of the column thickness (d), gob area span (D), boundary pillar thickness (h) and height of tailing gangue (H). The fitting degree between the four factors and the displacement of the gob roof (W) is reasonable because the correlation coefficient (R2) is greater than 0.9701. After establishing 29 groups that satisfy the principles of Box-Behnken design (BBD), the dry gangue tailings process was re-simulated for the selected sensitive interval. Using a combination of an analysis of variance (ANOVA), regression equations and a significance analysis, the prediction results of the response surface methodology (RSM) show that the significant degree for the stability of the mined-out area for the factors satisfies the relationship of h>D>d>H. The importance of the four factors cannot be disregarded in a comparison of the prediction results of the engineering test stope in the TLS mine. By comparing the data of monitoring points and function prediction, the proposed method has shown promising results, and the prediction accuracy of RSM model is acceptable. The relative errors of the two test stopes are 1.67% and 3.85%, respectively, which yield satisfactory reliability and reference values for the mines.

Key words

response surface methodology (RSM) Box-Behnken design (BBD) numerical simulation boundary pillar deformation prediction 

干堆排尾对露天坑坑下采场稳定性影响的响应面预测分析

摘要

科学分析露天坑下采空区稳定性是实现地下残矿安全高效回采的关键。 在对铜绿山矿地下残矿进行回采的同时, 利用上部露天坑回填干堆尾砂, 为研究该复杂条件下的采场变形问题, 采用单因素分析和 FLAC3D 数值软件, 运用函数拟合和回归分析将间柱宽度 d、 空区跨度 D、 境界顶柱厚度 h、 尾砂堆高 H 等 4 个影响因素对空区位移的影响进行敏感区间筛选, 结果表明 4 个因子与位移量 W 的拟合度较高, 相关系数 R2≥0.9701。 建立 29 组满足 Box-Behnken 中心组合设计原则的响应面数组, 对敏感区间下的尾砂堆积过程进行模拟, 并结合方差、 回归拟合及显著性分析, 得到影响采空区稳定性的显著程度依次为 h>D>d&H, 且 4 个均为不可忽视的重要因素。 最后, 将预测模型对工程实验采场变形情况进行预测分析及误差验证。 验证结果表明, 函数预测较好, 相对误差分别达到 1.67%、 3.85%, 具有较强的参考价值和应用可靠性。

关键词

响应面 Box-Behnken 中心组合设计 数值模拟 境界顶柱 变形预测 

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References

  1. [1]
    MASOUD Z N, RAFAEL J, REZA K. A new open-pit mine slope instability index defined using the improved rock engineering systems approach [J]. International Journal of Rock Mechanics & Mining Sciences. 2013, 61, 7: 1–14.Google Scholar
  2. [2]
    GOODFELLOW R C, DIMITRAKOPOULOS R. Global optimization of open pit mining complexes with uncertainty [J]. Applied Soft Computing, 2016, 40(4): 292–304.CrossRefGoogle Scholar
  3. [3]
    NABIOLLAH A, MAJID A, MEHDI R. Integration of sustainable development concepts in open pit mine design [J]. Journal of Cleaner Production, 2015, 108(12): 1037–1049.Google Scholar
  4. [4]
    GAO Yu-kun, BAO Na, ZHANG Ying-hua, JIANG Li-ming, HUANG Zhi-an. Research on index system of rock slope safety evaluation for open pit mine [J]. Procedia Engineering, 2011, 26: 1692–1697.CrossRefGoogle Scholar
  5. [5]
    SHI Xiu-zhi, HUANG Gang-hai, ZHANG Shu, ZHOU Jian. Goaf surrounding rock deformation and failure features using FLAC3D in underground mining shifted from open-pit in complex situation [J]. Journal of Central South University: Science and Technology, 2011, 42(6): 1710–1718. (in Chinese)Google Scholar
  6. [6]
    JIAO Y, HUDSON J A. The fully-coupled model for rock engineering systems [J]. International Journal of Rock Mechanics & Mining Sciences, 1995, 32(5): 491–512.CrossRefGoogle Scholar
  7. [7]
    SHISHVAN M S, SATTARVAND J. Long term production planning of open pit mines by ant colony optimization [J]. European Journal of Operational Research, 2015, 240(2): 825–836.CrossRefMATHGoogle Scholar
  8. [8]
    ZHOU Jian, LI Xi-bing, MITRI H S. Classification of rockburst in underground projects: Comparison of ten supervised learning methods [J]. Journal of Computing in Civil Engineering, 2016, 6: 04016003.CrossRefGoogle Scholar
  9. [9]
    ZHOU Jian, LI Xi-bing, MITRI H S. Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction [J]. Natural Hazards, 2015, 79(1): 291–316.CrossRefGoogle Scholar
  10. [10]
    CERYAN N, CERYAN S. An application of the interaction matrices method for slope failure susceptibility zoning: Dogankent settlement area (Giresun, NE Turkey) [J]. Bull Eng Geol Environ, 2008, 67(3): 375–385.CrossRefGoogle Scholar
  11. [11]
    ROZOS D, PYRGIOTIS L, SKIAS S. An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory: An application in Karditsa County [J]. Landslides, 2008, 5(3): 261–270.CrossRefGoogle Scholar
  12. [12]
    KHALOKAKAIE R, ZARE N M. Ranking the rock slope instability potential using the Interaction Matrix (IM) technique; a case study in Iran [J]. Arab J Geosci, 2012, 5(2): 263–273.CrossRefGoogle Scholar
  13. [13]
    ZHANG M S, ZHU W C, HOU Z S. Numerical simulation for determining the safe roof thickness and critical goaf span [J]. Journal of Mining & Safety Engineering, 2012, 29(4): 543–548. (in Chinese)Google Scholar
  14. [14]
    LI Di-yuang, LI Xi-bing, ZHAO Guo-yan. Roof security thickness determination of underground goaf under open-pit mine [J]. Opencast Mining Technology, 2005, 5: 17–20. (in Chinese)Google Scholar
  15. [15]
    ZHAO Y L, WU Q H, WANG W J. Strength reduction method to study stability of goaf overlapping roof based on catastrophe theory [J]. Chinese Journal of Rock Mechanic, 2010, 29(7): 1424–1434. (in Chinese)Google Scholar
  16. [16]
    WU Q H, PENG Z B, CHEN K P. Synthetic judgment on two-stage fuzzy of stability of mine gob area [J]. Journal of Central South University: Science and Technology, 2010, (7): 1424–1434. (in Chinese)Google Scholar
  17. [17]
    AMIR M, STEFAN C, IBRAHIM S. Inverse identification of flow stress in metal cutting process using response surface methodology [J]. Simulation Modelling Practice and Theory, 2016, 60(1): 40–53.Google Scholar
  18. [18]
    XU J L, WANG W C, LIANG H. Optimization of ionic liquid based ultrasonic assisted extraction of antioxidant compounds from Curcuma longa L. using response surface methodology [J]. Industrial Crops and Products, 2015, 76(12): 487–493.CrossRefGoogle Scholar
  19. [19]
    SRIMANTA R, JERALD A L. Using the Box–Benkhen design (BBD) to minimize the diameter of electrospun titanium dioxide nanofibers [J]. Chemical Engineering Journal, 2011, 169: 116–125.CrossRefGoogle Scholar
  20. [20]
    TAHEREH B G, RANJBAR A A. Geometry optimization of a nanofluid-based direct absorption solar collector using response surface methodology [J]. Solar Energy, 2015, 122(12): 314–325.Google Scholar
  21. [21]
    OSASAN K S, STACEY T R. Automatic prediction of time to failure of open pit mine slopes based on radar monitoring and inverse velocity method [J]. International Journal of Mining Science and Technology, 2014, 24(3): 275–280.CrossRefGoogle Scholar
  22. [22]
    FILIZ P, KADIR O. Partial collapses experienced for a steel space truss roof structure induced by ice ponds [J]. Engineering Failure Analysis, 2016, 60(2): 155–165.Google Scholar
  23. [23]
    PISKOTY G, WULLSCHLEGER L, LOSER R, HERWIG A, TUCHSCHMID M, TERRASI G. Failure analysis of a collapsed flat gymnasium roof [J]. Engineering Failure Analysis, 2014, 35(12): 104–113.Google Scholar
  24. [24]
    BLAAUWENDRAAD J. Ponding on light-weight flat roofs: strength and stability [J]. Engineering Structures, 2007, 29(5): 832–849.CrossRefGoogle Scholar
  25. [25]
    T K G, GREGORY A K. Storm duration effects on roof-to-wall-connection failures of a residential, wood-frame, gable roof [J]. Journal of Wind Engineering and Industrial Aerodynamics, 2014, 133: 101–109.CrossRefGoogle Scholar
  26. [26]
    WANG W, CHENG Y P, WANG H F. Fracture failure analysis of hard–thick sandstone roof and its controlling effect on gas emission in underground ultra-thick coal extraction [J]. Engineering Failure Analysis, 2015, 54: 150–162.CrossRefGoogle Scholar

Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xian-yang Qiu (邱贤阳)
    • 1
  • Jia-yao Chen (陈佳耀)
    • 1
  • Xiu-zhi Shi (史秀志)
    • 1
  • Shu Zhang (张舒)
    • 1
  • Jian Zhou (周健)
    • 1
  • Xin Chen (陈新)
    • 1
  1. 1.School of Resources and Safety EngineeringCentral South UniversityChangshaChina

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