Predicting Occupational Struck-by Incident Probability in Oil and Gas Industries: a Bayesian Network Model
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Abstract
Risk of injury or death due to occupational incidents in the oil and gas industries is higher than that of major incidents such as fire or explosion. In 2017, the largest proportion (36%) of fatalities and greatest number of incidents (24%) in the oil and gas industries were categorized as Struck-by. This study was aimed to develop a Bayesian network (BN) model for predicting occupational struck-by incident probability. Nineteen struck-by causal factors were extracted from the literature. Expert knowledge in addition to Dempster-Shafer theory was used to construct a BN. A questionnaire was developed to measure conditional probabilities of causal factors among participants. Struck-by probabilities of different states of causal factors were also estimated. The prior probability of struck-by incident was 3.09% (approximately 31 per 1000 operational workers per year). Belief updating predicted that preventing workers from being in improper position (in line of fire) would decrease the struck-by incidents by 37%. In contrary, failure of hazard warning (true state) and violation of procedures increased the struck-by probability by 4.08% (an increase of 32%) and 3.96% (an increase of 28%), respectively. The proposed BN model predicted that preventing workers from being in improper position (in line of fire) would decrease the struck-by occupational incidents by 37%. This approach was a step toward quantification of risks associated with occupational incidents. It had advantages including graphical representation of causal factors relationships, easily customizing model, and simply introducing of new evidence (belief updating).
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Hale, A., Walker, D., Walters, N., Bolt, H., 2012. Developing the understanding of underlying causes of construction fatal accidents. Saf. Sci. 50, 2020–2027. https://doi.org/10.1016/j.ssci.2012.01.018
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IOGP, 2017. Safety performance indicators – 2017 data. London.
Jacinto, C., Canoa, M., Guedes Soares, C., 2009. Workplace and organisational factors in accident analysis within the Food Industry. Saf. Sci. 47, 626–635. https://doi.org/10.1016/j.ssci.2008.08.002
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Kines, P., Lappalainen, J., Mikkelsen, K.L., Olsen, E., Pousette, A., Tharaldsen, J., Tómasson, K., Törner, M., 2011. Nordic Safety Climate Questionnaire (NOSACQ-50): A new tool for diagnosing occupational safety climate. Int. J. Ind. Ergon. 41, 634–646. https://doi.org/https://doi.org/10.1016/j.ergon.2011.08.004
Kujath, M.F., Amyotte, P.R., Khan, F.I., 2010. A conceptual offshore oil and gas process accident model. J. Loss Prev. Process Ind. 23, 323–330. https://doi.org/10.1016/j.jlp.2009.12.003
Lawshe, C.H., 1975. A Quantitative Approach To Content Validity. Pers. Psychol. 28, 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Marcot, B.G., 2012. Metrics for evaluating performance and uncertainty of Bayesian network models. Ecol. Modell. 230, 50–62. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2012.01.013
Mearns, K., Flin, R., Gordon, R., Fleming, M., 2001. Human and organizational factors in offshore safety. Work Stress 15, 144–160. https://doi.org/10.1080/026783701102678370110066616
Mohammadfam, I., Ghasemi, F., Kalatpour, O., Moghimbeigi, A., 2017. Constructing a Bayesian network model for improving safety behavior of employees at workplaces. Appl. Ergon. 58, 35–47. https://doi.org/10.1016/j.apergo.2016.05.006
Nguyen, L.D., Tran, D.Q., Chandrawinata, M.P., 2016. Predicting Safety Risk of Working at Heights Using Bayesian Networks. J. Constr. Eng. Manag. 142, 04016041. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001154
Probst, T.M., Brubaker, T.L., Barsotti, A., 2008. Organizational injury rate underreporting: The moderating effect of organizational safety climate. J. Appl. Psychol. https://doi.org/10.1037/0021-9010.93.5.1147
Rathnayaka, S., Khan, F., Amyotte, P., 2011. SHIPP methodology: Predictive accident modeling approach. Part I: Methodology and model description. Process Saf. Environ. Prot. 89, 151–164. https://doi.org/10.1016/j.psep.2011.01.002
Reason, J., 2000. Human error: models and management. BMJ 320, 768–770. https://doi.org/10.1136/bmj.320.7237.768
Sammarco, J., Gallagher, S., Mayton, A., Srednicki, J., 2012. A visual warning system to reduce struck-by or pinning accidents involving mobile mining equipment. Appl. Ergon. 43, 1058–1065. https://doi.org/10.1016/j.apergo.2012.03.006
Sentz, K., Ferson, S., 2002. Combination of evidence in Dempster-Shafer theory. Albuquerque.
Song, G., Khan, F., Wang, H., Leighton, S., Yuan, Z., Liu, H., 2016. Dynamic occupational risk model for offshore operations in harsh environments. Reliab. Eng. Syst. Saf. 150, 58–64. https://doi.org/10.1016/j.ress.2016.01.021
Stave, C., Törner, M., 2007. Exploring the organisational preconditions for occupational accidents in food industry: A qualitative approach. Saf. Sci. 45, 355–371. https://doi.org/10.1016/j.ssci.2006.07.001
Theophilus, S.C., Esenowo, V.N., Arewa, A.O., Ifelebuegu, A.O., Nnadi, E.O., Mbanaso, F.U., 2017. Human factors analysis and classification system for the oil and gas industry (HFACS-OGI). Reliab. Eng. Syst. Saf.
Xia, N., Zou, P.X.W., Liu, X., Wang, X., Zhu, R., 2018. A hybrid BN-HFACS model for predicting safety performance in construction projects. Saf. Sci. 101, 332–343. https://doi.org/10.1016/j.ssci.2017.09.025
Zohar, D., Polachek, T., 2014. Discourse-based intervention for modifying supervisory communication as leverage for safety climate and performance improvement: A randomized field study. J. Appl. Psychol. 99, 113–124. https://doi.org/10.1037/a0034096
Ale, B.J.M., Baksteen, H., Bellamy, L.J., Bloemhof, A., Goossens, L., Hale, A., Mud, M.L., Oh, J.I.H., Papazoglou, I.A., Post, J., Whiston, J.Y., 2008. Quantifying occupational risk: The development of an occupational risk model. Saf. Sci. 46, 176–185. https://doi.org/10.1016/j.ssci.2007.02.001
Attwood, D., Khan, F., Veitch, B., 2006. Occupational accident models—Where have we been and where are we going? J. Loss Prev. Process Ind. 19, 664–682. https://doi.org/10.1016/j.jlp.2006.02.001
Baksh, A.A., Khan, F., Gadag, V., Ferdous, R., 2015. Network based approach for predictive accident modelling. Saf. Sci. 80, 274–287. https://doi.org/10.1016/j.ssci.2015.08.003
Chuan, A.., 2006. Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: a comparison. J. Penyelid. IPBL.
Cox, S.J., Cheyne, A.J.T., 2000. Assessing safety culture in offshore environments. Saf. Sci. 34, 111–129. https://doi.org/https://doi.org/10.1016/S0925-7535(00)00009-6
Dzhambov, A., Dimitrova, D., 2017. Occupational Noise Exposure and the Risk for Work-Related Injury: A Systematic Review and Meta-analysis. Ann. Work Expo. Heal. 61, 1037–1053. https://doi.org/10.1093/annweh/wxx078
Esmaeili, B., Hallowell, M., 2012. Attribute-Based Risk Model for Measuring Safety Risk of Struck-By Accidents, in: Construction Research Congress 2012. American Society of Civil Engineers, Reston, VA, pp. 289–298. https://doi.org/10.1061/9780784412329.030
Ferry, T.S., 2007. Modern Accident Investigation and Analysis. John Wiley & Sons, Inc., Hoboken, NJ, USA. https://doi.org/10.1002/9780470172230
García-Herrero, S., Mariscal, M.A., García-Rodríguez, J., Ritzel, D.O., 2012. Working conditions, psychological/physical symptoms and occupational accidents. Bayesian network models. Saf. Sci. 50, 1760–1774. https://doi.org/10.1016/j.ssci.2012.04.005
Hale, A., Walker, D., Walters, N., Bolt, H., 2012. Developing the understanding of underlying causes of construction fatal accidents. Saf. Sci. 50, 2020–2027. https://doi.org/10.1016/j.ssci.2012.01.018
Hinze, J., Huang, X., Terry, L., 2005. The Nature of Struck-by Accidents. J. Constr. Eng. Manag. 131, 262–268. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:2(262)
Hurrell, J.J.J., McLaney, M.A., 1988. Exposure to job stress--a new psychometric instrument. Scand. J. Work. Environ. Health 27–28.
IOGP, 2017. Safety performance indicators – 2017 data. London.
Jacinto, C., Canoa, M., Guedes Soares, C., 2009. Workplace and organisational factors in accident analysis within the Food Industry. Saf. Sci. 47, 626–635. https://doi.org/10.1016/j.ssci.2008.08.002
Jensen, F. V., Nielsen, T.D., 2007. Bayesian Networks and Decision Graphs, Journal of Physics A: Mathematical and Theoretical, Information Science and Statistics. Springer New York, New York, NY. https://doi.org/10.1007/978-0-387-68282-2
Kines, P., Lappalainen, J., Mikkelsen, K.L., Olsen, E., Pousette, A., Tharaldsen, J., Tómasson, K., Törner, M., 2011. Nordic Safety Climate Questionnaire (NOSACQ-50): A new tool for diagnosing occupational safety climate. Int. J. Ind. Ergon. 41, 634–646. https://doi.org/https://doi.org/10.1016/j.ergon.2011.08.004
Kujath, M.F., Amyotte, P.R., Khan, F.I., 2010. A conceptual offshore oil and gas process accident model. J. Loss Prev. Process Ind. 23, 323–330. https://doi.org/10.1016/j.jlp.2009.12.003
Lawshe, C.H., 1975. A Quantitative Approach To Content Validity. Pers. Psychol. 28, 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Marcot, B.G., 2012. Metrics for evaluating performance and uncertainty of Bayesian network models. Ecol. Modell. 230, 50–62. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2012.01.013
Mearns, K., Flin, R., Gordon, R., Fleming, M., 2001. Human and organizational factors in offshore safety. Work Stress 15, 144–160. https://doi.org/10.1080/026783701102678370110066616
Mohammadfam, I., Ghasemi, F., Kalatpour, O., Moghimbeigi, A., 2017. Constructing a Bayesian network model for improving safety behavior of employees at workplaces. Appl. Ergon. 58, 35–47. https://doi.org/10.1016/j.apergo.2016.05.006
Nguyen, L.D., Tran, D.Q., Chandrawinata, M.P., 2016. Predicting Safety Risk of Working at Heights Using Bayesian Networks. J. Constr. Eng. Manag. 142, 04016041. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001154
Probst, T.M., Brubaker, T.L., Barsotti, A., 2008. Organizational injury rate underreporting: The moderating effect of organizational safety climate. J. Appl. Psychol. https://doi.org/10.1037/0021-9010.93.5.1147
Rathnayaka, S., Khan, F., Amyotte, P., 2011. SHIPP methodology: Predictive accident modeling approach. Part I: Methodology and model description. Process Saf. Environ. Prot. 89, 151–164. https://doi.org/10.1016/j.psep.2011.01.002
Reason, J., 2000. Human error: models and management. BMJ 320, 768–770. https://doi.org/10.1136/bmj.320.7237.768
Sammarco, J., Gallagher, S., Mayton, A., Srednicki, J., 2012. A visual warning system to reduce struck-by or pinning accidents involving mobile mining equipment. Appl. Ergon. 43, 1058–1065. https://doi.org/10.1016/j.apergo.2012.03.006
Sentz, K., Ferson, S., 2002. Combination of evidence in Dempster-Shafer theory. Albuquerque.
Song, G., Khan, F., Wang, H., Leighton, S., Yuan, Z., Liu, H., 2016. Dynamic occupational risk model for offshore operations in harsh environments. Reliab. Eng. Syst. Saf. 150, 58–64. https://doi.org/10.1016/j.ress.2016.01.021
Stave, C., Törner, M., 2007. Exploring the organisational preconditions for occupational accidents in food industry: A qualitative approach. Saf. Sci. 45, 355–371. https://doi.org/10.1016/j.ssci.2006.07.001
Theophilus, S.C., Esenowo, V.N., Arewa, A.O., Ifelebuegu, A.O., Nnadi, E.O., Mbanaso, F.U., 2017. Human factors analysis and classification system for the oil and gas industry (HFACS-OGI). Reliab. Eng. Syst. Saf.
Xia, N., Zou, P.X.W., Liu, X., Wang, X., Zhu, R., 2018. A hybrid BN-HFACS model for predicting safety performance in construction projects. Saf. Sci. 101, 332–343. https://doi.org/10.1016/j.ssci.2017.09.025
Zohar, D., Polachek, T., 2014. Discourse-based intervention for modifying supervisory communication as leverage for safety climate and performance improvement: A randomized field study. J. Appl. Psychol. 99, 113–124. https://doi.org/10.1037/a0034096
| Files | ||
| Issue | Vol 10 No 4 (2018) | |
| Section | Original Article(s) | |
| Published | 2018-12-24 | |
| Keywords | ||
| Bayesian network Incident prediction Oil industry Struck-by incident | ||
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How to Cite
1.
Shokouhi Y, Nassiri P, Mohammadfam I, Azam K. Predicting Occupational Struck-by Incident Probability in Oil and Gas Industries: a Bayesian Network Model. Int J Occup Hyg. 2018;10(4):236-249.
