IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i3p462-d135078.html
   My bibliography  Save this article

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques

Author

Listed:
  • Lluís Sanmiquel

    (ICL Chair in Sustainable Mining, Polytechnic University of Catalonia, 08034 Barcelona, Spain)

  • Marc Bascompta

    (ICL Chair in Sustainable Mining, Polytechnic University of Catalonia, 08034 Barcelona, Spain)

  • Josep M. Rossell

    (Department of Mathematics, Polytechnic University of Catalonia, 08034 Barcelona, Spain)

  • Hernán Francisco Anticoi

    (Department of Mining Engineering, Industrial and ICT, Polytechnic University of Catalonia, 08034 Barcelona, Spain)

  • Eduard Guash

    (Department of Mining Engineering, Industrial and ICT, Polytechnic University of Catalonia, 08034 Barcelona, Spain)

Abstract

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector—either surface or underground mining—based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.

Suggested Citation

  • Lluís Sanmiquel & Marc Bascompta & Josep M. Rossell & Hernán Francisco Anticoi & Eduard Guash, 2018. "Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques," IJERPH, MDPI, vol. 15(3), pages 1-11, March.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:3:p:462-:d:135078
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/3/462/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/3/462/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Galán, S.F. & Mosleh, A. & Izquierdo, J.M., 2007. "Incorporating organizational factors into probabilistic safety assessment of nuclear power plants through canonical probabilistic models," Reliability Engineering and System Safety, Elsevier, vol. 92(8), pages 1131-1138.
    2. Mallick, S. & Mukherjee, K., 1996. "An empirical study for mines safety management through analysis on potential for accident reduction," Omega, Elsevier, vol. 24(5), pages 539-550, October.
    3. Rivas, T. & Paz, M. & Martín, J.E. & Matías, J.M. & García, J.F. & Taboada, J., 2011. "Explaining and predicting workplace accidents using data-mining techniques," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 739-747.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anurag Yedla & Fatemeh Davoudi Kakhki & Ali Jannesari, 2020. "Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations," IJERPH, MDPI, vol. 17(19), pages 1-17, September.
    2. Lluís Sanmiquel & Marc Bascompta & Josep M. Rossell & Hernan Anticoi, 2021. "Analysis of Occupational Accidents in the Spanish Mining Sector in the Period 2009–2018," IJERPH, MDPI, vol. 18(24), pages 1-15, December.
    3. Rachel Aldred & Susana García-Herrero & Esther Anaya & Sixto Herrera & Miguel Ángel Mariscal, 2019. "Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment," IJERPH, MDPI, vol. 17(1), pages 1-16, December.
    4. Ahmet Tasdelen & Alper M. Özpinar, 2023. "A Dynamic Risk Analysis Model Based on Workplace Ergonomics and Demographic-Cognitive Characteristics of Workers," Sustainability, MDPI, vol. 15(5), pages 1-11, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chiming Guo & Shiyu Gong & Lin Tan & Bo Guo, 2012. "Extended GTST‐MLD for Aerospace System Safety Analysis," Risk Analysis, John Wiley & Sons, vol. 32(6), pages 1060-1071, June.
    2. Carole Duval & Geoffrey Fallet-Fidry & Benoît Iung & Philippe Weber & Eric Levrat, 2012. "A Bayesian network-based integrated risk analysis approach for industrial systems: application to heat sink system and prospects development," Journal of Risk and Reliability, , vol. 226(5), pages 488-507, October.
    3. Aneziris, O.N. & Topali, E. & Papazoglou, I.A., 2012. "Occupational risk of building construction," Reliability Engineering and System Safety, Elsevier, vol. 105(C), pages 36-46.
    4. A Léger & P Weber & E Levrat & C Duval & R Farret & B Iung, 2009. "Methodological developments for probabilistic risk analyses of socio-technical systems," Journal of Risk and Reliability, , vol. 223(4), pages 313-332, December.
    5. Pouya Gholizadeh & Ikechukwu S. Onuchukwu & Behzad Esmaeili, 2021. "Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013," IJERPH, MDPI, vol. 18(10), pages 1-24, May.
    6. Lluís Sanmiquel-Pera & Marc Bascompta & Hernán Francisco Anticoi, 2019. "Analysis of a Historical Accident in a Spanish Coal Mine," IJERPH, MDPI, vol. 16(19), pages 1-11, September.
    7. Gondia, Ahmed & Moussa, Ahmed & Ezzeldin, Mohamed & El-Dakhakhni, Wael, 2023. "Machine learning-based construction site dynamic risk models," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    8. Gerassis, S. & Albuquerque, M.T.D. & García, J.F. & Boente, C. & Giráldez, E. & Taboada, J. & Martín, J.E., 2019. "Understanding complex blasting operations: A structural equation model combining Bayesian networks and latent class clustering," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 195-204.
    9. Mei Liu & Boning Li & Hongjun Cui & Pin-Chao Liao & Yuecheng Huang, 2022. "Research Paradigm of Network Approaches in Construction Safety and Occupational Health," IJERPH, MDPI, vol. 19(19), pages 1-22, September.
    10. Justin Pence & Zahra Mohaghegh, 2020. "A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1183-1211, June.
    11. Mohamed Zul Fadhli Khairuddin & Puat Lu Hui & Khairunnisa Hasikin & Nasrul Anuar Abd Razak & Khin Wee Lai & Ahmad Shakir Mohd Saudi & Siti Salwa Ibrahim, 2022. "Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    12. Anurag Yedla & Fatemeh Davoudi Kakhki & Ali Jannesari, 2020. "Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations," IJERPH, MDPI, vol. 17(19), pages 1-17, September.
    13. Silva, Joaquim F. & Jacinto, Celeste, 2012. "Finding occupational accident patterns in the extractive industry using a systematic data mining approach," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 108-122.
    14. Rameez Rameezdeen & Abbas Elmualim, 2017. "The Impact of Heat Waves on Occurrence and Severity of Construction Accidents," IJERPH, MDPI, vol. 14(1), pages 1-13, January.
    15. Farcasiu, M. & Prisecaru, I., 2014. "MMOSA – A new approach of the human and organizational factor analysis in PSA," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 91-98.
    16. Jocelyn, Sabrina & Chinniah, Yuvin & Ouali, Mohamed-Salah & Yacout, Soumaya, 2017. "Application of logical analysis of data to machinery-related accident prevention based on scarce data," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 223-236.
    17. Hou, Lei & Wu, Xingguang & Wu, Zhuang & Wu, Shouzhi, 2020. "Pattern identification and risk prediction of domino effect based on data mining methods for accidents occurred in the tank farm," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    18. Pence, Justin & Sakurahara, Tatsuya & Zhu, Xuefeng & Mohaghegh, Zahra & Ertem, Mehmet & Ostroff, Cheri & Kee, Ernie, 2019. "Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 240-260.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:15:y:2018:i:3:p:462-:d:135078. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.