Skip to main content
SpringerLink
Log in

Hazard degree identification and coupling analysis of the influencing factors on goafs

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

In China, there are a large number of mined-out area which bring a great hidden danger to the mine enterprise’s safety production and people’s life and property. Therefore, the stability evaluation and the mechanism analysis of goafs have become a hot issue in the study on sustainable development of mining industry. To solve the complexity, concealment, and uncertainty of goaf influencing factors, 14 factors, i.e., rock mass structure, goaf span, exposed area, and so on, were selected as the evaluation indexes according to an iron ore. Then, the hazard evaluation model of goaf was established by using the information entropy and the unascertained measurement (UM) theory to identify the hazard degree, and the hazard importance degree index was put forward by changing the influencing factors and its index value to quantitatively analyze the coupling degree of influencing factors. This paper takes the BFZ-8 goaf as an example to evaluate and analyze the goaf stability. The results show that the evaluation model about UM and the experimental schemes are feasible and practicability, and the UM evaluation grades are consistent with the fuzzy evaluation grades and the actual risk grades in the case of multi-factor coupling. And the experimental results quantitatively reflect the coupling degree of the influencing factors by comparing the relative change rate of the importance degree, and the coupling results are consistent with the actual situation. So, the method can guide the production safety of mine, protect life and property safety of miners, and provide technical support and a new method for hazard degree identification of the goaf.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abrahamson NA, Bommer JJ (2012) Probability and uncertainty in seismic hazard analysis. Earthquake Spectra 21(2):603–607. doi:10.1193/1.1899158

    Article  Google Scholar 

  • Al Heib M, Duval C et al (2015) Analysis of the historical collapse of an abandoned underground chalk mine in 1961 in Clamart (Paris, France). Bull Eng Geol Environ 74:1001–1018. doi: 10.1007/s10064-014-0677-6

    Article  Google Scholar 

  • Allaire D, Willcox K (2009) Uncertainty assessment of complex models with application to aviation environmental policy-making. Transp Policy 34(4):109–113. doi: 10.1016/j.tranpol.2014.02.022

  • Burduk A, Stefaniak P (2012) Application of a perception artificial neural network for building the stability of a mining process. IDEAL, LNCS 7435:826–833. doi: 10.1007/978-3-642-32639-4_98

    Google Scholar 

  • Chen CM (2013) Research on evaluation method of eco-city construction based on unascertained measure theory. Tianjin University (in Chinese)

  • Chen JL (2014) A network security risk assessment model based on unascertained mathematics. J Air Force Eng Univ 15(2):91–94 (in Chinese)

    Google Scholar 

  • Freidin AM, Neverov SA, Neverov AA, Filippov PA (2008) Mine stability with application of sublevel caving schemes. J Min Sci 44:82–91. doi: 10.1007/s10913-008-0008-z

    Article  Google Scholar 

  • Fu JX, Song WD, Du JH (2013) Study of disturbance law for wall rock while goaf group formation in metal mines. Rock Soil Mech 34(Supp.1):508–515 (in Chinese)

    Google Scholar 

  • Gong FQ, Liu KW, Li ZG (2010) Bayes discriminant analysis method for subsidence risk prediction in mining goaf. Journal of Mining and Safety Engineering 1:30–39 (in Chinese)

    Google Scholar 

  • Hu YX, Li XB (2012) Bayes discriminant analysis method to identify risky of complicated goaf in mines and its application. Trans Nonferrous Met Soc China 22:425–431. doi: 10.1016/S1003-6326(11)61194-1

    Article  Google Scholar 

  • Hu JH, Shang JL, Zhou KP, Chen YK, Ning YL, Liu L, Aliyu MM (2015) Hazard degree identification of goafs based on scale effect of structure by RS-TOPSIS method. J Cent S Univ Technol 22:684–692. doi: 10.1007/s11771-015-2571-1

    Article  Google Scholar 

  • Huang H (2012) Evaluate enterprise resource planning based on rough-set unascertained model. In: Proceedings-2012 I.E. symposium on robotics and applications ISRA 2012 pp:506–510, doi: 10.1109/ISRA.2012.6219235

  • Jiang JP (2003) Research on engineering geology adaptability and treating effects of building freeway on surface above Yexi old goaf. Rock Soil Mech 24(Supp):439–442 (in Chinese)

    Google Scholar 

  • Li XB, Li DY (2006) Detection, treatment and safety evaluation of underground goaf in metal mine. Journal of Mining and Safety Engineering 1:24–29 (in Chinese)

    Google Scholar 

  • Li M, Zheng HC, Liu ZH, Wu XQ (2010) Effect analysis of the long-term strength of gypsum on mine goaf stability. Industrial Minerals & Processing 2:21–24 (in Chinese). doi: 10.16283/j.cnki.hgkwyjg.2010.02.006

    Google Scholar 

  • Li M, Liu ZH, Miao Q, Zheng HC, Zhang J (2013) Analysis of major influence factors to stability of mine-out area. Industrial Minerals & Processing 9:20–24 (in Chinese). doi: 10.16283/j.cnki.hgkwyjg.2013.09.012

    Google Scholar 

  • Li ZJ, Lin WQ, Chen Y (2015a) Risk assessment of goaf based on AGA-BP neural network. Science and Technology of Safety Production in China 7:135–141 (in Chinese)

    Google Scholar 

  • Li HM, Qin KL, Li P (2015b) Selection of project delivery approach with unascertained model. Kybernetes 44(2):238–252. doi: 10.1108/K-01-2014-0012

    Article  Google Scholar 

  • Liu DJ, Wang YC (2015) Study on stability and warning of goaf in Gongchangling open-pit iron mine. Journal of Safety Science and Technology 11(8):52–57 (in Chinese)

    Google Scholar 

  • Liu KD, Wu HQ, Pang YJ (1999) Mathematical treatment and application of uncertain information. Science Press, Beijing (in Chinese)

    Google Scholar 

  • Liu KD, Cao QK, Pang YJ (2004) A method of fault diagnosis based on unascertained set. Acta Automatic Sinica 30(5):747–756 (in Chinese). doi: 10.16383/j.aas.2004.05.013

    Google Scholar 

  • Liu AH, Dong L, Dong LJ (2010) Optimization model of unascertained measurement for underground mining method selection and its application. J Cent S Univ Technol 17:744–749. doi: 10.1007/s11771-010-0550-0

    Article  Google Scholar 

  • Lu P, Hudson JA (1993) A fuzzy evaluation approach to the stability of underground excavations. In: Proceedings of the ISRM symposium, EUROCK’93, pp 615–622

  • Luan TT, Xie ZH (2014) Risk evaluation model of waste dump landslide based on uncertainty measurement theory. Journal of Central South University (Natural Science Edition) 5:1613–1618 (in Chinese)

    Google Scholar 

  • Mafakheri F.,Dai L,Slezak D.,Nasiri F. (2007) Project delivery system selection under uncertainty: multi-criteria multi-level decision aid model. Journal of Construction Engineering and Management pp:200–206, doi: 10.1061/(ASCE)0742-597X (2007)23:4(200)

  • Nan SQ, Yang N (2012) Group stability analysis for goaf in transform from strip mining to underground mining based on CMS actual measuring. Hebei metallurgy 8:10–16 (in Chinese)

    Google Scholar 

  • Shin HS, Kwon YC, Jung YS, Bae GJ, Kim YG (2009) Methodology for quantitative hazard assessment for tunnel collapses based on case histories in Korea. Int J Rock Mech Min Sci 46:1072–1087. doi: 10.1016/j.ijrmms.2009.02.009

    Article  Google Scholar 

  • Swift GM, Reddish DJ (2002) Stability problems associated with an abandoned ironstone mine. Bull Eng Geol Env 61:227–239. doi: 10.1007/s10064-001-0147-9

  • Wang GY (1990) On unascertained information and its mathematical treatment. Journal of Harbin Institute of Architectural Engineering 23(4):52–58 (in Chinese)

    Google Scholar 

  • Wang HF, Li XB (2014) Stability classification of goaf based on support vector machine. Science and Technology of Safety Production in China 10:154–159 (in Chinese)

    Google Scholar 

  • Wang WD, Li JJ (2016) Highway traffic efficiency evaluation based on unascertained measure model. Journal of Zhejiang University (Engineering Edition) 1:48–54 (in Chinese). doi: 10.3785/j.issn.1008-973X.2016.01.008

    Google Scholar 

  • Wang YG, Zhang ZY (2015) Impact mechanism of groundwater flow on overlying strata movement in the goaf. South-to-North Water Transfers and Water Science & Technology 13(4):742–746 (in Chinese). doi: 10.13476/j.cnki.nsbdqk.2015.04.030

    Google Scholar 

  • Wang JA, Shang XC, Ma HT (2008) Investigation of catastrophic ground collapse in Xingtai gypsum mines in China. International Journal of Rock Mechanics & Mining Sciences 45(8):1480–1499. doi: 10.1016/j.ijrmms.2008.02.012

    Article  Google Scholar 

  • Wen TX, Sun HJ (2015) RS-SVM model for subsidence risk prediction in mining goaf. Science and Technology of Safety Production in China 10:16–21 (in Chinese)

    Google Scholar 

  • Xue JL, Wang XM, Liu Q, Yang J (2011) Underground goaf risk analysis evaluation based on AHP-gray clustering model. Hunan University of Science and Technology (Natural Science Edition) 2:5–10 (in Chinese)

    Google Scholar 

  • Zhang HB, Song WD (2014) Stability analysis and treatment of goaf in metal mine. Metallurgical industry press, Beijing (in Chinese)

    Google Scholar 

  • Zhang M, Nai L, Guo Y, Lv Y (2012a) Simulation on influence of fault on goaf surface subsidence. J Jilin Univ (Earth Sci Ed) 42(6):1834–1838 (in Chinese). doi: 10.13278/j.cnki.jjuese.2012.06.040

    Google Scholar 

  • Zhang HB, Song WD, Liu FF (2012b) Analysis on the stability of cavity based on cavity monitoring system. ICICA 2012, Part I,CCIS 307:646–652

    Google Scholar 

  • Zhang GW, Li FM, Li SZ, Deng KZ (2013) Change of subsidence factor on the law of rock mass rupture. J China Coal Soc 38(6):977–981 (in Chinese). doi: 10.13225/j.cnki.jccs.2013.06.022

    Google Scholar 

  • Zhao GY, Liang WZ, Hong CS (2015) Two dimensional evaluation of the improved cloud model for the stability of goaf. Science and Technology of Safety Production in China 10:102–108 (in Chinese)

    Google Scholar 

  • Zhao JN,Shi LN,Zhang L (2016) Application of improved unascertained mathematical modeling security evaluation of civil airport. Int J Syst Assur Eng Manag pp:1–12, doi: 10.1007/s13198-016-0417-3

  • Zhou J, Li XB, Hani S, Mitri, Wang SM, Wei W (2013a) Identification of large-scale goaf instability in underground mine using particle swarm optimization and support vector machine. Int J Min Sci Technol 23:701–707. doi: 10.1016/j.ijmst.2013.08.014

    Article  Google Scholar 

  • Zhou ZH, Hou KP, Ren FY (2013b) Stability analysis of large-scale mined-out area and its control methods in Paomaping lead-zinc deposit. Journal of Mining & Safety Engineering 30(6):863–867 (in Chinese)

    Google Scholar 

Download references

Acknowledgements

This work was funded by the National Natural Science Foundation of China (No. 41641036), the Project of Graduate Research and Innovation of Ordinary University in Jiangsu Province (No. CXZZ13_0936), and the NASS Key Laboratory of Land Environment and Disaster Monitoring (No. LEDM2014B04). The authors would also like to thank the anonymous reviewers for their comments and suggestions.

Author information

Authors and Affiliations

  1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, People’s Republic of China

    Hai-ping Xiao & Guang-li Guo

  2. School of Architectural and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, People’s Republic of China

    Hai-ping Xiao

  3. Hu’nan Institute of Surveying and Mapping of Geology, Hengyang, 421001, People’s Republic of China

    Wei Liu

Authors
  1. Hai-ping Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guang-li Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guang-li Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, Hp., Guo, Gl. & Liu, W. Hazard degree identification and coupling analysis of the influencing factors on goafs. Arab J Geosci 10, 68 (2017). https://doi.org/10.1007/s12517-017-2839-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-017-2839-x

Keywords

Search

Navigation