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An association rule mining model for the assessment of the correlations between the attributes of severe accidents

    Bilal Umut Ayhan Affiliation
    ; Neşet Berkay Doğan Affiliation
    ; Onur Behzat Tokdemir   Affiliation

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
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Apr 9, 2020
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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