Skip to main content
SpringerLink
Log in

Fuzzy risk prediction of roof fall and rib spalling: based on FFTA–DFCE and risk matrix methods

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

The working conditions of underground mining are complex and variable, and roof fall and rib spalling are one of the main types of accidents that can occur. Building an integrated model to evaluate the risk of roof fall and rib spalling is the foundation of mine safety. On the basis of the inherent attributes of event risk, the fuzzy evaluation set and probability of basic events are obtained by using the fuzzy fault tree analysis method based on the sample’s fuzzy information. Subsequently, the likelihood of roof fall and rib spalling is determined. Consequence severity data are obtained by using the dynamic fuzzy logic method, and the consequence severity grade of roof fall and rib spalling is evaluated via the dynamic fuzzy comprehensive evaluation method. The event risk level is determined by the risk matrix method. Roof fall and rib spalling in a non-coal mine is analyzed and evaluated by using fuzzy fault tree analysis and dynamic fuzzy comprehensive evaluation. The weak links in the operation of an underground mine are identified by fuzzy fault tree analysis as “mining process, roof management, support and reinforcement.” Then, the risk development trend is determined by the dynamic fuzzy comprehensive evaluation method. The risk matrix method is integrated to determine whether the risk level of the mine is “high risk, unacceptable” and expected to deteriorate in the future. The results show the validity and feasibility of the risk analysis and prediction model for roof fall and rib spalling.

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

Similar content being viewed by others

References

  • Ackermann F, Howick S, Quigley J, Walls L, Houghton T (2014) Systemic risk elicitation: using causal maps to engage stakeholders and build a comprehensive view of risks. Eur J Oper Res 238(1):290–299

    Article  Google Scholar 

  • Albery S, Borys D, Tepe S (2016) Advantages for risk assessment: evaluating learnings from question sets inspired by the FRAM and the risk matrix in a manufacturing environment. Saf Sci 89:180–189

    Article  Google Scholar 

  • Can GF, Toktas F (2018) A novel fuzzy risk matrix based risk assessment approach. Kybernetes 47(9):1721–1751

    Article  Google Scholar 

  • Cox LA (2008) What’s wrong with risk matrices? Risk Anal 28(2):497–512

    Article  Google Scholar 

  • Deng QG, Gao JL, Liu MJ (2011) Safety systems engineering (bilingual) (chapter 3). China Mining University Press, Xuzhou (In Chinese)

    Google Scholar 

  • Deng Q, Li MY (2012) Safety system management (Chapter 1). Northwest Polytechnic University Press, Xi’an (In Chinese)

    Google Scholar 

  • Duan YS, Zhao JG, Chen JP, Bai GY (2016) A risk matrix analysis method based on potential risk influence: a case study on cryogenic liquid hydrogen filling system. Process Saf Environ Prot 102:277–287

    Article  CAS  Google Scholar 

  • Elleuch H, Hachicha W, Chabchoub H (2014) A combined approach for supply chain risk management: description and application to a real hospital pharmaceutical case study. J Risk Res 17(5):641–663

    Article  Google Scholar 

  • Endrina N, Rasero JC, Konovessis D (2018) Risk analysis for RoPax vessels: a case of study for the Strait of Gibraltar. Ocean Eng 151:141–151

    Article  Google Scholar 

  • Garvey MD, Carnovale S, Yeniyurt S (2015) An analytical framework for supply network risk propagation: a Bayesian network approach. Eur J Oper Res 243(2):618–627

    Article  Google Scholar 

  • GB/T 27921 (2011) Risk management — risk assessment technology. Standards Press of China, Beijing http://www.doc88.com/p-3357762219908.html (In Chinese). Accessed 1 Feb 2012

  • Ghadge A, Dani S, Kalawsky R (2012) Supply chain risk management: present and future scope. Int J Logist Manag 23(3):313–339

    Article  Google Scholar 

  • Gürcanli GE, Güngen U (2009) An occupational safety risk analysis method at construction sites using fuzzy sets. Int J Ind Ergon 39(2):371–387

    Article  Google Scholar 

  • He BJ, Zhao DX, Zhu J, Darko A, Guo ZH (2018) Promoting and implementing urban sustainability in China: an integration of sustainable initiatives at different urban scales. Habitat Int 82:83–93

    Article  Google Scholar 

  • Heckmann I, Comes T, Nickel S (2015) A critical review on supply chain risk –definition, measure and modeling. Omega 52:119–132

    Article  Google Scholar 

  • Hsu WK, Lian SJ, Huang SH (2017) Risk assessment of operational safety for oil tankers - a revised risk matrix. J Navig 70(4):775–788

    Article  Google Scholar 

  • Jeong KS, Lee KW, Lim HK (2010) Risk assessment on hazards for decommissioning safety of a nuclear facility. Ann Nucl Energy 37(12):1751–1762

    Article  CAS  Google Scholar 

  • Lavasani SM, Ramzali N, Sabzalipour F, Akyuz E (2015) Utilization of fuzzy fault tree analysis (FFTA) for quantified risk analysis of leakage in abandoned oil and natural gas wells. Ocean Eng 108:729–737

    Article  Google Scholar 

  • Lavasani SM, Wang J, Yang Z, Finlay J (2011) Application of fuzzy fault tree analysis on oil and gas offshore pipelines. Int J Marine Sci Eng 1(1):29–42

    Google Scholar 

  • Levine ES (2012) Improving risk matrices: the advantages of logarithmically scaled axes. J Risk Res 15(2):209–222

    Article  Google Scholar 

  • Li WJ, Liang W, Zhang LB, Tang Q (2015) Performance assessment system of health, safety and environment based on experts’ weights and fuzzy comprehensive evaluation. J Loss Prev Process Ind 35:95–103

    Article  Google Scholar 

  • Long RS, Zhang JH (2017) Risk assessment method of UHV AC/DC power system under serious disasters. Energies 10(1):13–15

    Article  Google Scholar 

  • Lu LL, Liang W, Zhang LB, Zhang H, Zhang H, Shan JZ (2015) A comprehensive risk evaluation method for natural gas pipelines by combining a risk matrix with a bow-tie model. J Nat Gas Sci Eng 25:124–133

    Article  Google Scholar 

  • Markowski AS, Mannan MS (2008) Fuzzy risk matrix. J Hazard Mater 159(1):152–157

    Article  CAS  Google Scholar 

  • Mishra RK, Rinne M (2014) Guidelines to design the scope of a geotechnical risk assessment for underground mines. J Min Sci 50(4):745–756

    Article  Google Scholar 

  • Mou B, Li X, Qiao Q, He BJ, Wu ML (2019) Seismic behaviour of the corner joints of a frame under biaxial cyclic loading. Eng Struct 196:109316

    Article  Google Scholar 

  • Mottahedi A, Ataei M (2019) Fuzzy fault tree analysis for coal burst occurrence probability in underground coal mining. Tunn Undergr Space Technol 83:165–174

    Article  Google Scholar 

  • Nieto-Morote A, Ruz-Vila FA (2011) Fuzzy approach to construction project risk assessment. Int J Proj Manag 29(2):220–231

    Article  Google Scholar 

  • Pokoradi L (2002) Fuzzy logic-based risk assessment. AARMS 1(1):63–73

    Google Scholar 

  • Qazi A, Akhtar P, Wieland A (2018a) Risk matrix driven supply chain risk management: adapting risk matrix based tools to modelling interdependent risks and risk appetite. Computers and Industrial Engineering S0360835218303747-. https://www.sciencedirect.com/science/article/pii/S0360835218303747. Accessed 4 Aug 2018

  • Qazi A, Dickson A, Quigley J, Gaudenzi B (2018b) Supply chain risk network management: a Bayesian belief network and expected utility based approach for managing supply chain risks. Int J Prod Econ 196:24–42

    Article  Google Scholar 

  • Qazi A, Gaudenzi B (2016) Supply chain risk management: creating an agenda for future research. Int J Supp Chain a Oper Resilience 2(1):12–50

    Article  Google Scholar 

  • Qazi A, Quigley J, Dickson A, Ekici SO (2017) Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies. Eur J Oper Res 259(1):189–204

    Article  Google Scholar 

  • Shen YJ, Yan RX, Yang GS, Xu GL, Wang SY (2017) Comparisons of evaluation factors and application effects of the new [BQ] GSI system with international rock mass classification systems. Geotech Geol Eng 35(4):1–26

    CAS  Google Scholar 

  • Smith ED, Siefert WT, Drain D (2009) Risk matrix input data biases. Syst Eng 12(4):344–360

    Article  Google Scholar 

  • State Council of PRC (2007) Byelaw governing reporting, investigation and handling of accidents. The 172th Executive Meeting of the State Council of PRC. http://www.gov.cn/ziliao/flfg/2007-04/19/content_589264.htm (In Chinese). Accessed 4 April 2007

  • Sun J, Wang LG, Zhang HL, Shen YF (2009) Application of fuzzy neural network in predicting the risk of rock burst. Procedia Earth Planet Sci 1(1):536–543

    Article  Google Scholar 

  • Wang B, Wu C, Huang L, Zhang LB, Kang LG, Gao KX (2018a) Prevention and control of major accidents (MAs) and particularly serious accidents (PSAs) in the industrial domain in China: current status, recent efforts and future prospects. Process Saf Environ Prot 117(7):1–11

    CAS  Google Scholar 

  • Wang CL, Wu JD, Wang X, He X (2018b) Application of the hidden Markov model in a dynamic risk assessment of rainstorms in Dalian, China. Stoch Env Res Risk A 32(7):2045–2056

    Article  Google Scholar 

  • Wang D, Zhang P, Chen L (2013) Fuzzy fault tree analysis for fire and explosion of crude oil tanks. J Loss Prev Process Ind 26(6):1390–1398

    Article  Google Scholar 

  • Warren SN, Kallu RR, Barnard CK (2016) Correlation of the Rock Mass Rating (RMR) System with the Unified Soil Classification System (USCS): introduction of the Weak Rock Mass Rating System (W-RMR). Rock Mech Rock Eng 49(11):1–12

    Article  Google Scholar 

  • Wu KF, Sasidharan L, Thor CP, Chen SY (2018) Crash sequence based risk matrix for motorcycle crashes. Accid Anal Prev 117:21–31

    Article  Google Scholar 

  • Yan LJ, Zhang LB, Liang W, Li WJ, Du M (2017) Key factors identification and dynamic fuzzy assessment of health, safety and environment performance in petroleum enterprises. Saf Sci 94:77–84

    Article  Google Scholar 

  • Yazdi M, Nikfar F, Nasrabadi M (2017) Failure probability analysis by employing fuzzy fault tree analysis. Int J Syst Assur Eng Manag 8(2):1177–1193

    Article  Google Scholar 

  • Zhang K, Duan ML, Luo XL, Hou GX (2017) A fuzzy risk matrix method and its application to the installation operation of subsea collet connector. J Loss Prev Process Ind 45:147–159

    Article  Google Scholar 

  • Zhang P, Qin G, Wang Y (2018) Optimal maintenance decision method for urban gas pipelines based on as low as reasonably practicable principle. Sustainability 11(1):153

    Article  Google Scholar 

  • Zheng YS, Kang YJ, Niu XT (2018) Evaluation on human factor risk of abnormal train reception and departure based on game theory and extenics. J Safety Sci Technol 14(1):179–184 http://qikan.cqvip.com/Qikan/Article/Detail?id=674703809 (in Chinese)

    Google Scholar 

  • Zhang P, Qin G, Wang Y (2019) Risk assessment system for oil and gas pipelines laid in one ditch based on quantitative risk analysis. Energies 12(6):981

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank editors and reviewers for their positive and constructive suggestions and comments.

Funding

This work was supported by the National Key Technology Project of Prevention and Control of Major Accidents in Safety Production (Grant No. 149hubei-0002-2017AQ).

Author information

Authors and Affiliations

  1. School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, People’s Republic of China

    Wen Li, Yicheng Ye, Qihu Wang & Nanyan Hu

  2. Industrial Safety Engineering Technology Research Center of Hubei Province, Wuhan, 430081, Hubei, People’s Republic of China

    Yicheng Ye

  3. Sinosteel Wuhan Safety and Environmental Protection Research Institute Co., Ltd, Wuhan, 430081, Hubei, People’s Republic of China

    Xianhua Wang

Authors
  1. Wen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yicheng Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qihu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianhua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nanyan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qihu Wang.

Additional information

Responsible editor: Marcus Schulz

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, W., Ye, Y., Wang, Q. et al. Fuzzy risk prediction of roof fall and rib spalling: based on FFTA–DFCE and risk matrix methods. Environ Sci Pollut Res 27, 8535–8547 (2020). https://doi.org/10.1007/s11356-019-06972-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-019-06972-4

Keywords

Search

Navigation