An association rule mining model for the assessment of the correlations between the attributes of severe accidents
Abstract
Identifying the correlations between the attributes of severe accidents could be vital to preventing them. If such relationships were known dynamically, it would be possible to take preventative actions against accidents. The paper aims to develop an analytical model that is adaptable for each type of data to create preventative measures that will be suitable for any computational systems. The present model collectively shows the relationships between the attributes in a coherent manner to avoid severe accidents. In this respect, Association Rule Mining (ARM) is used as the technique to identify the correlations between the attributes. The research adopts a positivist approach to adhere to the factual knowledge concerning nine different accident types through case studies and quantitative measurements in an objective nature. ARM was exemplified with nine different types of construction accidents to validate the adaptability of the proposed model. The results show that each accident type has different characteristics with varying combinations of the attribute, and analytical model accomplished to accommodate variation through the dataset. Ultimately, professionals can identify the cause-effect relationships effectively and set up preventative measures to break the link between the accident causing factors.
Keyword : accident analysis, Association Rule Mining, data mining, network analysis
How to Cite
Ayhan, B. U. ., Doğan, N. B., & Tokdemir, O. B. (2020). An association rule mining model for the assessment of the correlations between the attributes of severe accidents. Journal of Civil Engineering and Management, 26(4), 315-330. https://doi.org/10.3846/jcem.2020.12316
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data(SIGMOD’93) (pp. 207–216). https://doi.org/10.1145/170035.170072
Aminbakhsh, S., Gunduz, M., & Sonmez, R. (2013). Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. Journal of Safety Research, 46, 99–105. https://doi.org/10.1016/j.jsr.2013.05.003
Ayhan, B. U., & Tokdemir, O. B. (2019a). Predicting the outcome of construction incidents. Safety Science, 113, 91–104. https://doi.org/10.1016/j.ssci.2018.11.001
Ayhan, B. U., & Tokdemir, O. B. (2019b). Safety assessment in megaprojects using artificial intelligence. Safety Science, 118, 273–287. https://doi.org/10.1016/j.ssci.2019.05.027
Ayhan, B. U., & Tokdemir, O. B. (2020). Accident analysis for construction safety using latent class clustering and artificial neural network. Journal of Construction Engineering and Management, 146(3), 04019114. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001762
Başağa, H. B., Temel, B. A., Atasoy, M., & Yıldırım, İ. (2018). A study on the effectiveness of occupational health and safety trainings of construction workers in Turkey. Safety Science, 110, 344–354. https://doi.org/10.1016/j.ssci.2018.09.002
Bavafa, A., Mahdiyar, A., & Marsono, A. K. (2018). Identifying and assessing the critical factors for effective implementation of safety programs in construction projects. Safety Science, 106, 47–56. https://doi.org/10.1016/j.ssci.2018.02.025
Camino López, M. A., Ritzel, D. O., Fontaneda, I., & González Alcantara, O. J. (2008). Construction industry accidents in Spain. Journal of Safety Research, 39(5), 497–507. https://doi.org/10.1016/j.jsr.2008.07.006
Chan, A. P. C., Javed, A. A., Lyu, S., Hon, C. K. H., & Wong, F. K. W. (2016). Strategies for improving safety and health of ethnic minority construction workers. Journal of Construction Engineering and Management,142(9). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001148
Chen, D., Xu, C., & Ni, S. (2017). Data mining on Chinese train accidents to derive associated rules. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 231(2), 239–252. https://doi.org/10.1177/0954409715624724
Cheng, C. W., Lin, C. C., & Leu, S. S. (2010). Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry. Safety Science, 48(4), 436–444. https://doi.org/10.1016/j.ssci.2009.12.005
Cheng, Y., Yu, W. Der, & Li, Q. (2015). GA-based multi-level association rule mining approach for defect analysis in the construction industry. Automation in Construction, 51, 78–91. https://doi.org/10.1016/j.autcon.2014.12.016
Choi, B., Jebelli, H., & Lee, S. H. (2019). Feasibility analysis of electrodermal activity (EDA) acquired from wearable sensors to assess construction workers’ perceived risk. Safety Science, 115, 110–120. https://doi.org/10.1016/j.ssci.2019.01.022
Cruz Rios, F., Chong, W. K., & Grau, D. (2017). The need for detailed gender-specific occupational safety analysis. Journal of Safety Research, 62, 53–62. https://doi.org/10.1016/j.jsr.2017.06.002
Das, S., & Sun, X. (2014). Investigating the pattern of traffic crashes under rainy weather by association rules in data mining. In Transportation Research Board 93rd Annual Meeting. Washington DC, USA.
Das, S., Dutta, A., Avelar, R., Dixon, K., Sun, X., & Jalayer, M. (2018). Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures. International Journal of Urban Sciences, 23(1), 30–48. https://doi.org/10.1080/12265934.2018.1431146
DiDomenico, A., McGorry, R. W., Huang, Y. H., & Blair, M. F. (2010). Perceptions of postural stability after transitioning to standing among construction workers. Safety Science, 48(2), 166–172. https://doi.org/10.1016/j.ssci.2009.07.006
Ding, L., Fang, W., Luo, H., Love, P. E. D., Zhong, B., & Ouyang, X. (2018). A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory. Automation in Construction, 86, 118–124. https://doi.org/10.1016/j.autcon.2017.11.002
Dong, X. S., Choi, S. D., Borchardt, J. G., Wang, X., & Largay, J. A. (2013). Fatal falls from roofs among U.S. construction workers. Journal of Safety Research, 44(1), 17–24. https://doi.org/10.1016/j.jsr.2012.08.024
Esmaeili, B., Hallowell, M. R., & Rajagopalan, B. (2015a). Attribute-based safety risk assessment. I: Analysis at the fundamental level. Journal of Construction Engineering and Management, 141(8), 04015021. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000980
Esmaeili, B., Hallowell, M. R., & Rajagopalan, B. (2015b). Attribute-based safety risk assessment. II: Predicting safety outcomes using generalized linear models. Journal of Construction Engineering and Management, 141(8), 04015022. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000981
Eteifa, S. O., & El-adaway, I. H. (2018). Using social network analysis to model the interaction between root causes of fatalities in the construction industry. Journal of Management in Engineering, 34(1), 04017045. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000567
Eurostat. (2015). Accidents at work statistics. https://ec.europa. eu/eurostat/statistics-explained/index.php/Accidents_at_work_statistics
Evanoff, B., Dale, A. M., Zeringue, A., Fuchs, M., Gaal, J., Lipscomb, H. J., & Kaskutas, V. (2016). Results of a fall prevention educational intervention for residential construction. Safety Science, 89, 301–307. https://doi.org/10.1016/j.ssci.2016.06.019
Fang, Q., Li, H., Luo, X., Ding, L., Rose, T. M., An, W., & Yu, Y. (2018). A deep learning-based method for detecting non-certified work on construction sites. Advanced Engineering Informatics, 35, 56–68. https://doi.org/10.1016/j.aei.2018.01.001
Gao, R., Chan, A. P. C., Lyu, S., Zahoor, H., & Utama, W. P. (2018). Investigating the difficulties of implementing safety practices in international construction projects. Safety Science, 108, 39–47. https://doi.org/10.1016/j.ssci.2018.04.018
Gerassis, S., Martín, J. E., Garcia, T. J., Saavedra, A., García, J. T., & Taboada, J., (2016). Bayesian decision tool for the analysis of occupational accidents in the construction of embankments. Journal of Construction Engineering and Management, 143(2), 04016093. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001225
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, 188, 195–204. https://doi.org/10.1016/j.ress.2019.03.032
Geurts, K., Wets, G., Brijs, T., & Vanhoof, K. (2012). Profiling high frequency associations rules accident locations using association rules. Transportation Research Record: Journal of the Transportation Research Board, 1840(1), 123–130. https://doi.org/10.3141/1840-14
Gephi 0.9.2. An open source software for exploring and manipulating networks (n.d.). https://gephi.org/
Glinskiy, V., Serga, L., Khvan, M., & Zaykov, K. (2016). Fuzzy neural networks in the assessment of environmental safety. Procedia CIRP, 40, 614–618. https://doi.org/10.1016/j.procir.2016.01.143
Grill, M., & Nielsen, K. (2019). Promoting and impeding safety – A qualitative study into direct and indirect safety leadership practices of constructions site managers. Safety Science, 114, 148–159. https://doi.org/10.1016/j.ssci.2019.01.008
Guo, S., Zhang, P., & Ding, L. (2019). Time-statistical laws of workers’ unsafe behavior in the construction industry: A case study. Physica A: Statistical Mechanics and its Applications, 515, 419–429. https://doi.org/10.1016/j.physa.2018.09.091
Heinrich, H. (1959). Industrial accident prevention. NewYork: McGraw-Hill.
Jebelli, H., Ahn, C. R., & Stentz, T. L. (2016). Fall risk analysis of construction workers using inertial measurement units: Validating the usefulness of the postural stability metrics in construction. Safety Science, 84, 161–170. https://doi.org/10.1016/j.ssci.2015.12.012
Kaskutas, V., Dale, A. M., Lipscomb, H., & Evanoff, B. (2013). Fall prevention and safety communication training for foremen: Report of a pilot project designed to improve residential construction safety. Journal of Safety Research, 44(1), 111–118. https://doi.org/10.1016/j.jsr.2012.08.020
Kazan, E., & Usmen, M. A. (2018). Worker safety and injury severity analysis of earthmoving equipment accidents. Journal of Safety Research, 65, 73–81. https://doi.org/10.1016/j.jsr.2018.02.008
Kheni, N. A., Gibb, A. G. F., & Dainty, A. R. J. (2010). Health and safety management within small- and medium-sized enterprises (SMEs) in developing countries: Study of contextual influences. Journal of Construction Engineering and Management, 136(10), 1104–1115. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000218
Kim, Y. A., Ryoo, B. Y., Kim, Y.-S., & Huh, W. C. (2012). Major accident factors for effective safety management of highway construction projects. Journal of Construction Engineering and Management, 139(6), 628–640. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000640
Liao, C. W., & Chiang, T. L. (2016). Reducing occupational injuries attributed to inattentional blindness in the construction industry. Safety Science, 89, 129–137. https://doi.org/10.1016/j.ssci.2016.06.010
Liao, C. W., & Perng, Y. H. (2008). Data mining for occupational injuries in the Taiwan construction industry. Safety Science, 46(7), 1091–1102. https://doi.org/10.1016/j.ssci.2007.04.007
Liao, P. C., Chen, H., & Luo, X. (2019). Fusion model for hazard association network development: A case in elevator installation and maintenance. KSCE Journal of Civil Engineering, 23(4), 1451–1465. https://doi.org/10.1007/s12205-019-0646-5
Lin, C.-L., & Fan, C.-L. (2018). Examining association between construction inspection grades and critical defects using data mining and fuzzy logic. Journal of Civil Engineering and Management, 24(4), 301–317. https://doi.org/10.3846/jcem.2018.3072
Loosemore, M., & Malouf, N. (2019). Safety training and positive safety attitude formation in the Australian construction industry. Safety Science, 113, 233–243. https://doi.org/10.1016/j.ssci.2018.11.029
Melo, R. R. S. de, Costa, D. B., Álvares, J. S., & Irizarry, J. (2017). Applicability of unmanned aerial system (UAS) for safety inspection on construction sites. Safety Science, 98, 174–185. https://doi.org/10.1016/j.ssci.2017.06.008
Mistikoglu, G., Gerek, I. H., Erdis, E., Mumtaz Usmen, P. E., Cakan, H., & Kazan, E. E. (2015). Decision tree analysis of construction fall accidents involving roofers. Expert Systems with Applications, 42(4), 2256–2263. https://doi.org/10.1016/j.eswa.2014.10.009
Mirabadi, A., & Sharifian, S. (2010). Application of association rules in Iranian Railways (RAI) accident data analysis. Safety Science, 48, 1427–1435. https://doi.org/10.1016/j.ssci.2010.06.006
Mohammadi, A., Tavakolan, M., & Khosravi, Y. (2018). Factors influencing safety performance on construction projects: A review. Safety Science, 109, 382–397. https://doi.org/10.1016/j.ssci.2018.06.017
Mohandes, S. R., & Zhang, X. (2019). Towards the development of a comprehensive hybrid fuzzy-based occupational risk assessment model for construction workers. Safety Science, 115, 294–309. https://doi.org/10.1016/j.ssci.2019.02.018
Ning, X., Qi, J., & Wu, C. (2018). A quantitative safety risk assessment model for construction site layout planning. Safety Science, 104, 246–259. https://doi.org/10.1016/j.ssci.2018.01.016
Olson, R., Varga, A., Cannon, A., Jones, J., Gilbert-Jones, I., & Zoller, E. (2016). Toolbox talks to prevent construction fatalities: Empirical development and evaluation. Safety Science, 86, 122–131. https://doi.org/10.1016/j.ssci.2016.02.009
Patel, D. A., & Jha, K. N. (2014). Neural network approach for safety climate prediction. Journal of Management in Engineering, 31(6), UNSP 05014027. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000348
Patel, D. A., & Jha, K. N. (2016). Evaluation of construction projects based on the safe work behavior of co-employees through a neural network model. Safety Science, 89, 240–248. https://doi.org/10.1016/j.ssci.2016.06.020
Project Management Institute (PMI). (2008). A guide to the project management body of knowledge (PMBOK Guide) (4th ed.). Newtown Square, PA, USA.
Rapidminer Studio 9.2.0. Data science, machine learning, predictive analytics. (n.d.). https://rapidminer.com/
Reason, J., (1990). Human error. Cambridge University Press. https://doi.org/10.1017/CBO9781139062367
Rubio-Romero, J. C., Rubio, M. C., & García-Hernández, C. (2012). Analysis of construction equipment safety in temporary work at height. Journal of Construction Engineering and Management, 139(1), 9–14. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000567
RStudio. (2019). Integrated development environment for R. (Computer Software). http://www.rstudio.com/
Schoenfisch, A., Lipscomb, H., Silverstein, B., Cameron, W., & Adams, D. (2014). Rates of and circumstances surrounding work-related falls from height among union drywall carpenters in Washington State, 1989–2008. Journal of Safety Research, 51, 117–124. https://doi.org/10.1016/j.jsr.2014.09.007
Shao, B., Hu, Z., Liu, Q., Chen, S., & He, W. (2019). Fatal accident patterns of building construction activities in China. Safety Science, 111, 253–263. https://doi.org/10.1016/j.ssci.2018.07.019
Shin, D. P., Park, Y. J., Seo, J., & Lee, D. E. (2018). Association rules mined from construction accident data. KSCE Journal of Civil Engineering, 22(4), 1027–1039. https://doi.org/10.1007/s12205-017-0537-6
Stiles, S., Ryan, B., & Golightly, D. (2018). Evaluating attitudes to safety leadership within rail construction projects. Safety Science, 110, 134–144. https://doi.org/10.1016/j.ssci.2017.12.030
Suárez-Cebador, M., Rubio-Romero, J. C., & López-Arquillos, A. (2014). Severity of electrical accidents in the construction industry in Spain. Journal of Safety Research, 48, 63–70. https://doi.org/10.1016/j.jsr.2013.12.002
Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., & Bowman, D. (2017). Construction safety clash detection: Identifying safety incompatibilities among fundamental attributes using data mining. Automation in Construction, 74, 39–54. https://doi.org/10.1016/j.autcon.2016.11.001
Tokdemir, O. B., & Ayhan, B. U. (2019). The analysis of accidents with contact of sharp objects by using analytic hierarchy process and artificial neural networks. DÜMF Journal of Engineering, 10(1), 323–334. https://doi.org/10.24012/dumf.466493
U.S Bureau of Labor Statistics (2017). Injuries, illnesses, and fatalities. https://www.bls.gov/iif/
Verma, A., Khan, S. Das, Maiti, J., & Krishna, O. B. (2014). Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports. Safety Science, 70, 89–98. https://doi.org/10.1016/j.ssci.2014.05.007
Weng, J., Zhu, J. Z., Yan, X., & Liu, Z. (2016). Investigation of work zone crash casualty patterns using association rules. Accident Analysis and Prevention, 92, 43–52. https://doi.org/10.1016/j.aap.2016.03.017
Wimer, B., Pan, C., Lutz, T., Hause, M., Warren, C., Dong, R., & Xu, S. (2017). Evaluating the stability of a freestanding Mast Climbing Work Platform. Journal of Safety Research, 62, 163–172. https://doi.org/10.1016/j.jsr.2017.06.014
Winge, S. & Albrechtsen, E. (2018). Accident types and barrier failures in the construction industry. Safety Science, 105, 158–166. https://doi.org/10.1016/j.ssci.2018.02.006
Winge, S., Albrechtsen, E., & Mostue, B. A. (2019). Causal factors and connections in construction accidents. Safety Science, 112, 130–141. https://doi.org/10.1016/j.ssci.2018.10.015
Xu, C., Bao, J., Wang, C., & Liu, P. (2018). Association rule analysis of factors contributing to extraordinarily severe traffic crashes in China. Journal of Safety Research, 67, 65–75. https://doi.org/10.1016/j.jsr.2018.09.013
Yao, Z., Deng, W., & Wu, D. (2018). Association rule analysis of contributory factors to severe traffic accidents. In 18th COTA International Conference of Transportation Professionals (pp. 1886–1873). https://doi.org/10.1061/9780784481523.186
Yiu, N. S. N., Sze, N. N., & Chan, D. W. M. (2018). Implementation of safety management systems in Hong Kong construction industry – A safety practitioner’s perspective. Journal of Safety Research, 64, 1–9. https://doi.org/10.1016/j.jsr.2017.12.011
Zhang, P., Lingard, H., Blismas, N., Wakefield, R., & Kleiner, B. (2015). Work-health and safety-risk perceptions of construction-industry stakeholders using photograph-based Q methodology. Journal of Construction Engineering and Management, 141(5), 04014093. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000954
Zhang, X., & Liu, Z. (2011). Analysis of multi-dimensional association rule in marine casualties. In First International Conference on Transportation Information and Safety (ICTIS) (pp. 2697–2705). https://doi.org/10.1061/41177(415)340
Zhao, D., McCoy, A. P., Kleiner, B. M., Mills, T. H., & Lingard, H. (2016). Stakeholder perceptions of risk in construction. Safety Science, 82, 111–119. https://doi.org/10.1016/j.ssci.2015.09.002
Aminbakhsh, S., Gunduz, M., & Sonmez, R. (2013). Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. Journal of Safety Research, 46, 99–105. https://doi.org/10.1016/j.jsr.2013.05.003
Ayhan, B. U., & Tokdemir, O. B. (2019a). Predicting the outcome of construction incidents. Safety Science, 113, 91–104. https://doi.org/10.1016/j.ssci.2018.11.001
Ayhan, B. U., & Tokdemir, O. B. (2019b). Safety assessment in megaprojects using artificial intelligence. Safety Science, 118, 273–287. https://doi.org/10.1016/j.ssci.2019.05.027
Ayhan, B. U., & Tokdemir, O. B. (2020). Accident analysis for construction safety using latent class clustering and artificial neural network. Journal of Construction Engineering and Management, 146(3), 04019114. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001762
Başağa, H. B., Temel, B. A., Atasoy, M., & Yıldırım, İ. (2018). A study on the effectiveness of occupational health and safety trainings of construction workers in Turkey. Safety Science, 110, 344–354. https://doi.org/10.1016/j.ssci.2018.09.002
Bavafa, A., Mahdiyar, A., & Marsono, A. K. (2018). Identifying and assessing the critical factors for effective implementation of safety programs in construction projects. Safety Science, 106, 47–56. https://doi.org/10.1016/j.ssci.2018.02.025
Camino López, M. A., Ritzel, D. O., Fontaneda, I., & González Alcantara, O. J. (2008). Construction industry accidents in Spain. Journal of Safety Research, 39(5), 497–507. https://doi.org/10.1016/j.jsr.2008.07.006
Chan, A. P. C., Javed, A. A., Lyu, S., Hon, C. K. H., & Wong, F. K. W. (2016). Strategies for improving safety and health of ethnic minority construction workers. Journal of Construction Engineering and Management,142(9). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001148
Chen, D., Xu, C., & Ni, S. (2017). Data mining on Chinese train accidents to derive associated rules. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 231(2), 239–252. https://doi.org/10.1177/0954409715624724
Cheng, C. W., Lin, C. C., & Leu, S. S. (2010). Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry. Safety Science, 48(4), 436–444. https://doi.org/10.1016/j.ssci.2009.12.005
Cheng, Y., Yu, W. Der, & Li, Q. (2015). GA-based multi-level association rule mining approach for defect analysis in the construction industry. Automation in Construction, 51, 78–91. https://doi.org/10.1016/j.autcon.2014.12.016
Choi, B., Jebelli, H., & Lee, S. H. (2019). Feasibility analysis of electrodermal activity (EDA) acquired from wearable sensors to assess construction workers’ perceived risk. Safety Science, 115, 110–120. https://doi.org/10.1016/j.ssci.2019.01.022
Cruz Rios, F., Chong, W. K., & Grau, D. (2017). The need for detailed gender-specific occupational safety analysis. Journal of Safety Research, 62, 53–62. https://doi.org/10.1016/j.jsr.2017.06.002
Das, S., & Sun, X. (2014). Investigating the pattern of traffic crashes under rainy weather by association rules in data mining. In Transportation Research Board 93rd Annual Meeting. Washington DC, USA.
Das, S., Dutta, A., Avelar, R., Dixon, K., Sun, X., & Jalayer, M. (2018). Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures. International Journal of Urban Sciences, 23(1), 30–48. https://doi.org/10.1080/12265934.2018.1431146
DiDomenico, A., McGorry, R. W., Huang, Y. H., & Blair, M. F. (2010). Perceptions of postural stability after transitioning to standing among construction workers. Safety Science, 48(2), 166–172. https://doi.org/10.1016/j.ssci.2009.07.006
Ding, L., Fang, W., Luo, H., Love, P. E. D., Zhong, B., & Ouyang, X. (2018). A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory. Automation in Construction, 86, 118–124. https://doi.org/10.1016/j.autcon.2017.11.002
Dong, X. S., Choi, S. D., Borchardt, J. G., Wang, X., & Largay, J. A. (2013). Fatal falls from roofs among U.S. construction workers. Journal of Safety Research, 44(1), 17–24. https://doi.org/10.1016/j.jsr.2012.08.024
Esmaeili, B., Hallowell, M. R., & Rajagopalan, B. (2015a). Attribute-based safety risk assessment. I: Analysis at the fundamental level. Journal of Construction Engineering and Management, 141(8), 04015021. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000980
Esmaeili, B., Hallowell, M. R., & Rajagopalan, B. (2015b). Attribute-based safety risk assessment. II: Predicting safety outcomes using generalized linear models. Journal of Construction Engineering and Management, 141(8), 04015022. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000981
Eteifa, S. O., & El-adaway, I. H. (2018). Using social network analysis to model the interaction between root causes of fatalities in the construction industry. Journal of Management in Engineering, 34(1), 04017045. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000567
Eurostat. (2015). Accidents at work statistics. https://ec.europa. eu/eurostat/statistics-explained/index.php/Accidents_at_work_statistics
Evanoff, B., Dale, A. M., Zeringue, A., Fuchs, M., Gaal, J., Lipscomb, H. J., & Kaskutas, V. (2016). Results of a fall prevention educational intervention for residential construction. Safety Science, 89, 301–307. https://doi.org/10.1016/j.ssci.2016.06.019
Fang, Q., Li, H., Luo, X., Ding, L., Rose, T. M., An, W., & Yu, Y. (2018). A deep learning-based method for detecting non-certified work on construction sites. Advanced Engineering Informatics, 35, 56–68. https://doi.org/10.1016/j.aei.2018.01.001
Gao, R., Chan, A. P. C., Lyu, S., Zahoor, H., & Utama, W. P. (2018). Investigating the difficulties of implementing safety practices in international construction projects. Safety Science, 108, 39–47. https://doi.org/10.1016/j.ssci.2018.04.018
Gerassis, S., Martín, J. E., Garcia, T. J., Saavedra, A., García, J. T., & Taboada, J., (2016). Bayesian decision tool for the analysis of occupational accidents in the construction of embankments. Journal of Construction Engineering and Management, 143(2), 04016093. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001225
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, 188, 195–204. https://doi.org/10.1016/j.ress.2019.03.032
Geurts, K., Wets, G., Brijs, T., & Vanhoof, K. (2012). Profiling high frequency associations rules accident locations using association rules. Transportation Research Record: Journal of the Transportation Research Board, 1840(1), 123–130. https://doi.org/10.3141/1840-14
Gephi 0.9.2. An open source software for exploring and manipulating networks (n.d.). https://gephi.org/
Glinskiy, V., Serga, L., Khvan, M., & Zaykov, K. (2016). Fuzzy neural networks in the assessment of environmental safety. Procedia CIRP, 40, 614–618. https://doi.org/10.1016/j.procir.2016.01.143
Grill, M., & Nielsen, K. (2019). Promoting and impeding safety – A qualitative study into direct and indirect safety leadership practices of constructions site managers. Safety Science, 114, 148–159. https://doi.org/10.1016/j.ssci.2019.01.008
Guo, S., Zhang, P., & Ding, L. (2019). Time-statistical laws of workers’ unsafe behavior in the construction industry: A case study. Physica A: Statistical Mechanics and its Applications, 515, 419–429. https://doi.org/10.1016/j.physa.2018.09.091
Heinrich, H. (1959). Industrial accident prevention. NewYork: McGraw-Hill.
Jebelli, H., Ahn, C. R., & Stentz, T. L. (2016). Fall risk analysis of construction workers using inertial measurement units: Validating the usefulness of the postural stability metrics in construction. Safety Science, 84, 161–170. https://doi.org/10.1016/j.ssci.2015.12.012
Kaskutas, V., Dale, A. M., Lipscomb, H., & Evanoff, B. (2013). Fall prevention and safety communication training for foremen: Report of a pilot project designed to improve residential construction safety. Journal of Safety Research, 44(1), 111–118. https://doi.org/10.1016/j.jsr.2012.08.020
Kazan, E., & Usmen, M. A. (2018). Worker safety and injury severity analysis of earthmoving equipment accidents. Journal of Safety Research, 65, 73–81. https://doi.org/10.1016/j.jsr.2018.02.008
Kheni, N. A., Gibb, A. G. F., & Dainty, A. R. J. (2010). Health and safety management within small- and medium-sized enterprises (SMEs) in developing countries: Study of contextual influences. Journal of Construction Engineering and Management, 136(10), 1104–1115. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000218
Kim, Y. A., Ryoo, B. Y., Kim, Y.-S., & Huh, W. C. (2012). Major accident factors for effective safety management of highway construction projects. Journal of Construction Engineering and Management, 139(6), 628–640. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000640
Liao, C. W., & Chiang, T. L. (2016). Reducing occupational injuries attributed to inattentional blindness in the construction industry. Safety Science, 89, 129–137. https://doi.org/10.1016/j.ssci.2016.06.010
Liao, C. W., & Perng, Y. H. (2008). Data mining for occupational injuries in the Taiwan construction industry. Safety Science, 46(7), 1091–1102. https://doi.org/10.1016/j.ssci.2007.04.007
Liao, P. C., Chen, H., & Luo, X. (2019). Fusion model for hazard association network development: A case in elevator installation and maintenance. KSCE Journal of Civil Engineering, 23(4), 1451–1465. https://doi.org/10.1007/s12205-019-0646-5
Lin, C.-L., & Fan, C.-L. (2018). Examining association between construction inspection grades and critical defects using data mining and fuzzy logic. Journal of Civil Engineering and Management, 24(4), 301–317. https://doi.org/10.3846/jcem.2018.3072
Loosemore, M., & Malouf, N. (2019). Safety training and positive safety attitude formation in the Australian construction industry. Safety Science, 113, 233–243. https://doi.org/10.1016/j.ssci.2018.11.029
Melo, R. R. S. de, Costa, D. B., Álvares, J. S., & Irizarry, J. (2017). Applicability of unmanned aerial system (UAS) for safety inspection on construction sites. Safety Science, 98, 174–185. https://doi.org/10.1016/j.ssci.2017.06.008
Mistikoglu, G., Gerek, I. H., Erdis, E., Mumtaz Usmen, P. E., Cakan, H., & Kazan, E. E. (2015). Decision tree analysis of construction fall accidents involving roofers. Expert Systems with Applications, 42(4), 2256–2263. https://doi.org/10.1016/j.eswa.2014.10.009
Mirabadi, A., & Sharifian, S. (2010). Application of association rules in Iranian Railways (RAI) accident data analysis. Safety Science, 48, 1427–1435. https://doi.org/10.1016/j.ssci.2010.06.006
Mohammadi, A., Tavakolan, M., & Khosravi, Y. (2018). Factors influencing safety performance on construction projects: A review. Safety Science, 109, 382–397. https://doi.org/10.1016/j.ssci.2018.06.017
Mohandes, S. R., & Zhang, X. (2019). Towards the development of a comprehensive hybrid fuzzy-based occupational risk assessment model for construction workers. Safety Science, 115, 294–309. https://doi.org/10.1016/j.ssci.2019.02.018
Ning, X., Qi, J., & Wu, C. (2018). A quantitative safety risk assessment model for construction site layout planning. Safety Science, 104, 246–259. https://doi.org/10.1016/j.ssci.2018.01.016
Olson, R., Varga, A., Cannon, A., Jones, J., Gilbert-Jones, I., & Zoller, E. (2016). Toolbox talks to prevent construction fatalities: Empirical development and evaluation. Safety Science, 86, 122–131. https://doi.org/10.1016/j.ssci.2016.02.009
Patel, D. A., & Jha, K. N. (2014). Neural network approach for safety climate prediction. Journal of Management in Engineering, 31(6), UNSP 05014027. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000348
Patel, D. A., & Jha, K. N. (2016). Evaluation of construction projects based on the safe work behavior of co-employees through a neural network model. Safety Science, 89, 240–248. https://doi.org/10.1016/j.ssci.2016.06.020
Project Management Institute (PMI). (2008). A guide to the project management body of knowledge (PMBOK Guide) (4th ed.). Newtown Square, PA, USA.
Rapidminer Studio 9.2.0. Data science, machine learning, predictive analytics. (n.d.). https://rapidminer.com/
Reason, J., (1990). Human error. Cambridge University Press. https://doi.org/10.1017/CBO9781139062367
Rubio-Romero, J. C., Rubio, M. C., & García-Hernández, C. (2012). Analysis of construction equipment safety in temporary work at height. Journal of Construction Engineering and Management, 139(1), 9–14. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000567
RStudio. (2019). Integrated development environment for R. (Computer Software). http://www.rstudio.com/
Schoenfisch, A., Lipscomb, H., Silverstein, B., Cameron, W., & Adams, D. (2014). Rates of and circumstances surrounding work-related falls from height among union drywall carpenters in Washington State, 1989–2008. Journal of Safety Research, 51, 117–124. https://doi.org/10.1016/j.jsr.2014.09.007
Shao, B., Hu, Z., Liu, Q., Chen, S., & He, W. (2019). Fatal accident patterns of building construction activities in China. Safety Science, 111, 253–263. https://doi.org/10.1016/j.ssci.2018.07.019
Shin, D. P., Park, Y. J., Seo, J., & Lee, D. E. (2018). Association rules mined from construction accident data. KSCE Journal of Civil Engineering, 22(4), 1027–1039. https://doi.org/10.1007/s12205-017-0537-6
Stiles, S., Ryan, B., & Golightly, D. (2018). Evaluating attitudes to safety leadership within rail construction projects. Safety Science, 110, 134–144. https://doi.org/10.1016/j.ssci.2017.12.030
Suárez-Cebador, M., Rubio-Romero, J. C., & López-Arquillos, A. (2014). Severity of electrical accidents in the construction industry in Spain. Journal of Safety Research, 48, 63–70. https://doi.org/10.1016/j.jsr.2013.12.002
Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., & Bowman, D. (2017). Construction safety clash detection: Identifying safety incompatibilities among fundamental attributes using data mining. Automation in Construction, 74, 39–54. https://doi.org/10.1016/j.autcon.2016.11.001
Tokdemir, O. B., & Ayhan, B. U. (2019). The analysis of accidents with contact of sharp objects by using analytic hierarchy process and artificial neural networks. DÜMF Journal of Engineering, 10(1), 323–334. https://doi.org/10.24012/dumf.466493
U.S Bureau of Labor Statistics (2017). Injuries, illnesses, and fatalities. https://www.bls.gov/iif/
Verma, A., Khan, S. Das, Maiti, J., & Krishna, O. B. (2014). Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports. Safety Science, 70, 89–98. https://doi.org/10.1016/j.ssci.2014.05.007
Weng, J., Zhu, J. Z., Yan, X., & Liu, Z. (2016). Investigation of work zone crash casualty patterns using association rules. Accident Analysis and Prevention, 92, 43–52. https://doi.org/10.1016/j.aap.2016.03.017
Wimer, B., Pan, C., Lutz, T., Hause, M., Warren, C., Dong, R., & Xu, S. (2017). Evaluating the stability of a freestanding Mast Climbing Work Platform. Journal of Safety Research, 62, 163–172. https://doi.org/10.1016/j.jsr.2017.06.014
Winge, S. & Albrechtsen, E. (2018). Accident types and barrier failures in the construction industry. Safety Science, 105, 158–166. https://doi.org/10.1016/j.ssci.2018.02.006
Winge, S., Albrechtsen, E., & Mostue, B. A. (2019). Causal factors and connections in construction accidents. Safety Science, 112, 130–141. https://doi.org/10.1016/j.ssci.2018.10.015
Xu, C., Bao, J., Wang, C., & Liu, P. (2018). Association rule analysis of factors contributing to extraordinarily severe traffic crashes in China. Journal of Safety Research, 67, 65–75. https://doi.org/10.1016/j.jsr.2018.09.013
Yao, Z., Deng, W., & Wu, D. (2018). Association rule analysis of contributory factors to severe traffic accidents. In 18th COTA International Conference of Transportation Professionals (pp. 1886–1873). https://doi.org/10.1061/9780784481523.186
Yiu, N. S. N., Sze, N. N., & Chan, D. W. M. (2018). Implementation of safety management systems in Hong Kong construction industry – A safety practitioner’s perspective. Journal of Safety Research, 64, 1–9. https://doi.org/10.1016/j.jsr.2017.12.011
Zhang, P., Lingard, H., Blismas, N., Wakefield, R., & Kleiner, B. (2015). Work-health and safety-risk perceptions of construction-industry stakeholders using photograph-based Q methodology. Journal of Construction Engineering and Management, 141(5), 04014093. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000954
Zhang, X., & Liu, Z. (2011). Analysis of multi-dimensional association rule in marine casualties. In First International Conference on Transportation Information and Safety (ICTIS) (pp. 2697–2705). https://doi.org/10.1061/41177(415)340
Zhao, D., McCoy, A. P., Kleiner, B. M., Mills, T. H., & Lingard, H. (2016). Stakeholder perceptions of risk in construction. Safety Science, 82, 111–119. https://doi.org/10.1016/j.ssci.2015.09.002