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Explaining and predicting workplace accidents using data-mining techniques

Author

Listed:
  • Rivas, T.
  • Paz, M.
  • Martín, J.E.
  • Matías, J.M.
  • García, J.F.
  • Taboada, J.

Abstract

Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:7:p:739-747
    DOI: 10.1016/j.ress.2011.03.006
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    Citations

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    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. 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.
    3. 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).
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.

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