Análisis bayesiano de factores de riesgo de accidente en trabajos de movimientos de tierras

  1. J. F. García 1
  2. J. E. Martín 2
  3. S. Gerassis 2
  4. A. Saavedra 2
  5. J. Taboada García 2
  1. 1 CIPP Internacional, S. L., Gijón, Asturias, España
  2. 2 Universidad de Vigo, España
Journal:
Informes de la construcción

ISSN: 0020-0883

Year of publication: 2017

Volume: 69

Issue: 546

Type: Article

DOI: 10.3989/IC.15.154 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Informes de la construcción

Abstract

This paper analyses the characteristics of earthmoving operations involving a workplace accident. Bayesian networks were used to identify the factors that best predicted potential risk situations. Inference studies were then conducted to analyse the interplay between different risk factors. We demonstrate the potential of Bayesian networks to describe workplace contexts and predict risk situations from a safety and production planning perspective.

Bibliographic References

  • (1) Swuste, P., Frijters, A., Guldenmund, F. (2012). Is it possible to influence safety in the building sector? A literature review extending from 1980 until the present. Safety Science, 50(5): 1333-1343. https://doi.org/10.1016/j.ssci.2011.12.036
  • (2) Feng, Y., Zhang, S., Wu, P. (2015). Factors influencing workplace accident costs of building projects. Safety Science, 72: 97-104. https://doi.org/10.1016/j.ssci.2014.08.008
  • (3) Holte, K. A., Kjestveit, K., Lipscomb, H. J. (2015). Company size and differences in injury prevalence among apprentices in building and construction in Norway. Safety Science, 71: 205-212. https://doi.org/10.1016/j.ssci.2014.01.007
  • (4) Lee, H. S., Kim, H., Park, M., Ai Lin Teo, E., Lee, K. P. (2012). Construction risk assessment using site influence factors. Journal of Computing in Civil Engineering, 26(3): 319-330. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000146
  • (5) Park, J., Park, S., Oh, T. (2015). The development of a web-based construction safety management information system to improve risk assessment. Journal of Civil Engineering, 19(3): 528-537. https://doi.org/10.1007/s12205-014-0664-2
  • (6) Neapolitan, R. E. (2004). Learning Bayesian networks. Prentice Hall.
  • (7) Rivas, T., Matías, J. M., Taboada, J., Argüelles, A. (2007). Application of Bayesian networks to the evaluation of roofing slate quality. Engineering Geology, 94: 27-37. https://doi.org/10.1016/j.enggeo.2007.06.002
  • (8) Martín, J. E., Rivas, T., Matías, J. M., Taboada, J., Argüelles, A. (2009). A Bayesian network analysis of workplace accidents caused by falls from a height. Safety Science, 47: 206-214. https://doi.org/10.1016/j.ssci.2008.03.004
  • (9) Li, L., Wang, J., Leung, H., Jiang, C. (2010). Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data. Risk Analysis: An International Journal, 30(7): 1157-1175.
  • (10) 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, 96: 739-747. https://doi.org/10.1016/j.ress.2011.03.006
  • (11) Leu, S. S., Chang, C. M. (2013). Bayesian-network-based safety risk assessment for steel construction projects. Accident Analysis and Prevention, 54: 122-123. https://doi.org/10.1016/j.aap.2013.02.019 PMid:23499984
  • (12) GeNIe & SMILE (2015). Structural Modeling, Inference, and Learning Engine. Decision Systems Laboratory, University of Pittsburgh, https://www.bayesfusion.com/.