Correlation analysis and prevention of electrocution risk factors in the construction industry

• Autorzy: Li Jue , Chen Feifei , Li Shije

Identyfikatory

DOI

10.24425/ace.2022.143053

• Języki publikacji: EN

Abstrakty

EN

Electrocution is one of the main causes of workplace deaths in the construction industry. This paper presents a framework for identifying electrocution risk factors and exploring the correlations between them, with the aim of assisting accident prevention research. Specifically, the Haddon Matrix is used to extract the risk factors from 193 investigation reports of electrical shock accidents from 2012-2019, and the Apriori algorithm is applied to examine the potential relationships between these factors. Based on association rules using three criteria: support (S), confidence (C) and lift (L), the betweenness centrality is then introduced to optimize association rules and find the most important rules though comparison. The results show that after optimization, some of these critical rules rise significantly in rank, such as Workplace: indoor → No CPR provided. Through these ranking changes, the focus of safety management is clarified, and finally, based on a comprehensive analysis of association rules, targeted accident prevention measures are suggested.

Słowa kluczowe

EN

accidents prevention; association rule; betweenness centrality; correlation analysis; electrocution; Haddon's matrix; construction industry

PL

zapobieganie wypadkom; reguła asocjacji; centralność; analiza korelacji; porażenie prądem; macierz Haddona; budownictwo

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2022
• Tom: Vol. 68, nr 4
• Strony: 537--554
• Opis fizyczny: Bibliogr. 52 poz., il., tab.

Twórcy

autor

Li Jue

• School of Traffic and Transportation Engineering, Changsha University of Science &Technology, Changsha, Hunan, P.R.China

• https://orcid.org/0000-0002-7245-4891

autor

Chen Feifei

• School of Traffic and Transportation Engineering, Changsha University of Science &Technology, Changsha, Hunan, P.R.China

• https://orcid.org/0000-0003-1994-4418

autor

Li Shije

• School of Traffic and Transportation Engineering, Changsha University of Science &Technology, Changsha, Hunan, P.R.China

• https://orcid.org/0000-0002-2759-6604

Bibliografia

• [1] S. Winge, E. Albrechtsen, B.A. Mostue, “Causal factors and connections in construction accidents”, Safety Science, 2019, vol. 112, pp. 130-141; DOI: 10.1016/j.ssci.2018.10.015.
• [2] ILO, Safety in Numbers: Pointers for a Global Safety Culture at Work. Geneva, Switzerland: International Labor Office, 2003, pp. 3-14.
• [3] O.A. Jannadi, M.S. Bu-Khamsin, “Safety factors considered by industrial contractors in Saudi Arabia”, Building and Environment, 2002, vol. 37, no. 5, pp. 539-547; DOI: 10.1016/S0360-1323(01)00056-7.
• [4] J. San, “Re-ring the safety production alarm”, Informatization of China Construction, 2018, no. 16, pp. 4 (in Chinese).
• [5] W. Li, “Characteristic Analysis of Housing and Municipal Works Accidents in China between 2012 and 2018”, M.A. thesis, Department of Management Engineering, Shandong Jianzhu University, Jinan, Shandong, China, 2019.
• [6] N. Ichikawa, Statistical accident analysis and electrical fatality rate in Japan, 2002-2011-Causes and preventive measures of fatal electrical accidents. IEEE IAS Electrical Safety Workshop, Louisville, 2015, pp. S1-S5.
• [7] M. Suárez-Cebador, J.C. Rubio-Romero, A. López-Arquillos, “Severity of electrical accidents in the construction industry in Spain”, Journal of Safety Research, 2014, vol. 48, pp. 63-70; DOI: 10.1016/j.jsr.2013.12.002.
• [8] F.Y.Y. Ling, M. Liu, Y.C. Woo, “Construction fatalities in Singapore”, International Journal of Project Management, 2009, vol. 27, no. 7, pp. 717-726; DOI: 10.1016/j.ijproman.2008.11.002.
• [9] P. Panaro, S. Amatucci, “Analysis of work accidents with electrical origin”, in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe, Milan. IEEE, 2017, pp. 1-4.
• [10] D.K. Neitzel, “Electrical safety when using temporary power”, in Petroleum and Chemical Industry Technical Conference (PCIC), Philadelphia. 2016, pp. 1-8.
• [11] D.K. Neitzel, “Electrical safety when working near overhead power lines”, in IEEE PES 13th International Conference on Transmission & Distribution Construction, Operation & Live-Line Maintenance (ESMO), Columbus. IEEE, 2016, pp. 1-5.
• [12] D.R. Crow, D.P. Liggett, J.E. Mitchem, F. Work, “Design and build electrical safety into construction projects”, in IEEE Petroleum and Chemical Industry Committee Conference (PCIC), Houston. IEEE, 2015, pp. 1-6.
• [13] N. El-Sherif, J.R. Lahera, “Improving safety culture among non-electrical workers”, in IEEE IAS Electrical Safety Workshop (ESW), Fort Worth. IEEE, 2018, pp. 1-6.
• [14] D.K. Neitzel, “Identifying the requirements for Qualified, Unqualified, and Competent Persons electrical safety training”, IEEE Transactions on Industry Applications, 2017, vol. 54, no. 1, pp. 5-9; DOI: 10.1109/TIA.2017.2741445.
• [15] D. Zhao, A.P. McCoy, B.M. Kleiner, J. Du, T.L. Smith-Jackson, “Decision-making chains in electrical safety for construction workers”, Journal of Construction Engineering and Management, 2016, vol. 142, no. 1, pp. 1-11; DOI: 10.1061/(ASCE)CO.1943-7862.0001037.
• [16] D. Zhao, A.P. McCoy, B.M. Kleiner, et al., “Sociotechnical systems of fatal electrical injuries in the construction industry”, Journal of Construction Engineering and Management, 2016, vol. 142, no. 1, pp. 1-10; DOI: 10.1061/(ASCE)CO.1943-7862.0001036.
• [17] J. Li, H. Liu, “Association of causes and intervention countermeasures of electric shock accidents based on HFACS”, Journal of Changsha University of Science & Technology (Natural Science), 2019, vol. 16, no. 2, pp. 36-41; DOI: 10.3969/j.issn.1672-9331.2019.02.007.
• [18] L.Y. Chang, H.W. Wang, “Analysis of traffic injury severity: An application of non-parametric classification tree techniques”, Accident Analysis & Prevention, 2006, vol. 38, no. 5, pp. 1019-1027; DOI: 10.1016/j.aap.2006.04.009.
• [19] R.J. Cleary, “Applied Data Mining: Statistical Methods for Business and Industry”, Journal of the American Statistical Association, 2012, vol. 101, no. 475, pp. 1317-1318; DOI: 10.1198/jasa.2006.s135.
• [20] R. Agrawal, T. Imieliński, A. Swami, “Mining association rules between sets of items in large databases”, in 1993 ACM SIGMOD International Conference on Management of Data, Washington DC. 1993, pp. 207-216.
• [21] H. Jiang, J. Cai, “Research on application of mining association rules in education evaluation”, presented at 2013 International Conference on Advanced ICT and Education (ICAICTE-13), Hainan, 2013.
• [22] S. Doddi, A. Marathe, S.S. Ravi, D.C. Torney, “Discovery of association rules in medical data”, Medical Informatics and the Internet in Medicine, 2001, vol. 26, no. 1, pp. 25-33; DOI: 10.1080/14639230117529.
• [23] M. Shahbaz, M. Srinivas, J.A. Harding, M. Turner, “Product design and manufacturing process improvement using association rules”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2006, vol. 220, no. 2, pp. 243-254; DOI: 10.1243/095440506X78183.
• [24] H. Anysz, P. Włodarek, P. Olszewski, S. Cafiso, “Identifying factors and conditions contributing to cyclists’ serious accidents with the use of association analysis”, Archives of Civil Engineering, 2021, vol. 67, no. 3, pp. 197-211; DOI: 10.24425/ace.2021.138051.
• [25] D.P. Shin, Y.J. Park, J. Seo, D.E. Lee, “Association rules mined from construction accident data”, KSCE Journal of Civil Engineering, 2018, vol. 22, no. 4, pp. 1027-1039; DOI: 10.1007/s12205-017-0537-6.
• [26] Y. Cheng, W. Yu, Q. Li, “Automation in Construction GA-based multi-level association rule mining approach for defect analysis in the construction industry”, Automation in Construction, 2015, vol. 51, pp. 78-91; DOI: 10.1016/j.autcon.2014.12.016.
• [27] A. Verma, S.D. Khan, J. Maiti, O.B. Krishna, “Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports”, Safety Science, 2014, vol. 70, pp. 89-98; DOI: 10.1016/j.ssci.2014.05.007.
• [28] K. Masoumi, A. Forouzan, H. Barzegari, et al., “Effective factors in severity of traffic accident-related traumas; an epidemiologic study based on the Haddon matrix”, Emergency, 2016, vol. 4, no. 2, pp. 78-82. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4893755/. [Accessed: Spr. 2016].
• [29] P.A. Hume, A.V. Lorimer, P.C. Griffiths, et al., “Recreational snow-sports injury risk factors and countermeasures: a meta-analysis review and Haddon matrix evaluation”, Sports Medicine, 2015, vol. 45, no. 8, pp. 1175-1190; DOI: 10.1007/s40279-015-0334-7.
• [30] L. Xiong, D. Bruck, M. Ball, “Utilization of the Haddon matrix to organize factors of survived accidental residential fires: frequencies for human, agent, and environment-related variables”, Fire Safety Science, 2014, vol. 11, pp. 1049-1062; DOI: 10.3801/IAFSS.FSS.11-1049.
• [31] S. Seyedebrahimi, A. Mirjalili, A. Sadeghian, “Identification and prioritization of factors influencing the increase in construction costs of building investments using factor analysis”, Archives of Civil Engineering, 2021, vol. 67, no. 3, pp. 705-722; DOI: 10.24425/ace.2021.138079.
• [32] S. Zheng, Y. Fan, Z. Li, “Causal factors of truck traffic accidents based on HFACS model”, Safety and Environmental Engineering, 2020, vol. 27, no. 6, pp. 133-139; DOI: 10.13578/j.cnki.issn.1671-1556.2020.06.019.
• [33] J. Li, J. Wang, N. Xu, Y. Hu, C. Cui, “Importance degree research of safety risk management processes of urban rail transit based on text mining method”, Information, 2018, vol. 9, no. 2; DOI: 10.3390/info9020026.
• [34] O. Čokorilo, M. De Luca, G. Dell’Acqua, “Aircraft safety analysis using clustering algorithms”, Journal of Risk Research, 2014, vol. 17, no. 10, pp. 1325-1340; DOI: 10.1080/13669877.2013.879493.
• [35] H. Fan, H. Li, “Retrieving similar cases for alternative dispute resolution in construction accidents using text mining techniques”, Automation in Construction, 2013, vol. 34, pp. 85-91; DOI: 10.1016/j.autcon.2012.10.014.
• [36] L. Sanmiquel, J.M. Rossell, C. Vintró, “Study of Spanish mining accidents using data mining techniques”, Safety Science, 2015, vol. 75, pp. 49-55; DOI: 10.1016/j.ssci.2015.01.016.
• [37] L. Tanguy, N. Tulechki, A. Urieli, et al., “Natural language processing for aviation safety reports: from classification to interactive analysis”, Computers in Industry, 2016, vol. 78, pp. 80-95; DOI: 10.1016/j.compind.2015.09.005.
• [38] J. Bondy, H. Lipscomb, K. Guarini, J.E. Glazner, “Methods for using narrative text from injury reports to identify factors contributing to construction injury”, American Journal of Industrial Medicine, 2005, vol. 48, no. 5, pp. 373-380; DOI: 10.1002/ajim.20228.
• [39] J. Du, M. El-Gafy, “Virtual organizational imitation for construction enterprises: Agent-based simulation framework for exploring human and organizational implications in construction management”, Journal of Computing in Civil Engineering, 2012, vol. 26, no. 3, pp. 282-297; DOI: 10.1061/(ASCE)CP.1943-5487.0000122.
• [40] M. Pineault, M. Rossignol, R.G. Barr, “Interrater analysis of a classification scheme of occupational fatalities by electrocution”, Journal of Safety Research, 1994, vol. 25, no. 2, pp. 107-115; DOI: 10.1016/0022-4375(94)90022-1.
• [41] T. Martin, X. Zhang, M.E. Newman, “Localization and centrality in networks”, Physical Review E, 2014, vol. 90, no. 5, art. ID 052808; DOI: 10.1103/PhysRevE.90.052808.
• [42] S.P. Borgatti, M.G. Everett, “A graph-theoretic perspective on centrality”, Social Networks, 2006, vol. 28, no. 4, pp. 466-484; DOI: 10.1016/j.socnet.2005.11.005.
• [43] A. Zaheer, G.G. Bell, “Benefiting from network position: firm capabilities, structural holes, and performance”, Strategic Management Journal, 2005, vol. 26, no. 9, pp. 809-825; DOI: 10.1002/smj.482.
• [44] X.X. Zheng, “Research on the Thermal Power Plant Operation Optimization Value Determining Based on Association Rule”, M.A. thesis, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing, China, 2011.
• [45] J. Han, J. Pei, M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
• [46] S. Rehman, A. Sharma, “Privacy Preserving Data Mining Using Association Rule Based on Apriori Algorithm”, in International Conference on Advanced Informatics for Computing Research, Singapore. 2017, pp. 218-226.
• [47] S. Brin, R. Motwani, C. Silverstein, “Beyond market baskets: Generalizing association rules to correlations”, in 1997 ACM SIGMOD International Conference on Management of Data, Tucson. 1997, pp. 265-276.
• [48] J. Väisänen, “The potential and usage of search engine marketing (SEM) in Finnish SMEs”, Ph.D. thesis, Faculty of Business and Technology Management, Tampere University of Technology, Tampere, Finland, 2010.
• [49] N. Nenonen, “Analysing factors related to slipping, stumbling, and falling accidents at work: Application of data mining methods to Finnish occupational accidents and diseases statistics database”, Applied Ergonomics, 2013, vol. 44, no. 2, pp. 215-224; DOI: 10.1016/j.apergo.2012.07.001.
• [50] J.Q. Hu, X.Y. Zhang, Z.Q. Wu, “Research on associated early-warning and visualization of hidden danger in en-terprise production based on TF-IDF”, China Safety Science Journal, 2019, vol. 29, no. 07, pp. 170-176; DOI: 10.16265/j.cnki.issn1003-3033.2019.07.027.
• [51] H. Kim, J. Lewko, E. Garritano, et al., “Construction fatality due to electrical contact in Ontario, Canada, 1997-2007”, Work, 2016, vol. 54, no. 3, pp. 639-646; DOI: 10.3233/WOR-162336.
• [52] M. Rossignol, M. Pineault, “Classification of fatal occupational electrocutions”, Canadian Journal of Public Health= Revue Canadienne de Sante Publique, 1994, vol. 85, no. 5, pp. 322-325.