Análisis bayesiano de factores de riesgo de accidente en trabajos de movimientos de tierras
- J. F. García 1
- J. E. Martín 2
- S. Gerassis 2
- A. Saavedra 2
- J. Taboada García 2
- 1 CIPP Internacional, S. L., Gijón, Asturias, España
- 2 Universidad de Vigo, España
ISSN: 0020-0883
Year of publication: 2017
Volume: 69
Issue: 546
Type: Article
More publications in: Informes de la construcción
Abstract
En este trabajo se analizan características de distintas obras en las que se ejecutaban trabajos de movimiento de tierras y tuvo lugar un accidente. Aplicando redes bayesianas se identifican los factores de mayor potencial predictivo de las situaciones de riesgo analizadas. Posteriormente se realizan estudios de inferencia para analizar la interrelación entre los distintos factores. Con todo esto se demuestra que las redes bayesianas pueden ser herramientas muy potentes en la descripción general de contextos de obra, y de gran capacidad predictiva dentro de la planificación de obras desde la perspectiva seguridad-producción.
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/.