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


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.
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Apr 9, 2020
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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).

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.

Ayhan, B. U., & Tokdemir, O. B. (2019a). Predicting the outcome of construction incidents. Safety Science, 113, 91–104.

Ayhan, B. U., & Tokdemir, O. B. (2019b). Safety assessment in megaprojects using artificial intelligence. Safety Science, 118, 273–287.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Gephi 0.9.2. An open source software for exploring and manipulating networks (n.d.).

Glinskiy, V., Serga, L., Khvan, M., & Zaykov, K. (2016). Fuzzy neural networks in the assessment of environmental safety. Procedia CIRP, 40, 614–618.

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.

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.

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.

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.

Kazan, E., & Usmen, M. A. (2018). Worker safety and injury severity analysis of earthmoving equipment accidents. Journal of Safety Research, 65, 73–81.

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.

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.

Liao, C. W., & Chiang, T. L. (2016). Reducing occupational injuries attributed to inattentional blindness in the construction industry. Safety Science, 89, 129–137.

Liao, C. W., & Perng, Y. H. (2008). Data mining for occupational injuries in the Taiwan construction industry. Safety Science, 46(7), 1091–1102.

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.

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.

Loosemore, M., & Malouf, N. (2019). Safety training and positive safety attitude formation in the Australian construction industry. Safety Science, 113, 233–243.

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.

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.

Mirabadi, A., & Sharifian, S. (2010). Application of association rules in Iranian Railways (RAI) accident data analysis. Safety Science, 48, 1427–1435.

Mohammadi, A., Tavakolan, M., & Khosravi, Y. (2018). Factors influencing safety performance on construction projects: A review. Safety Science, 109, 382–397.

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.

Ning, X., Qi, J., & Wu, C. (2018). A quantitative safety risk assessment model for construction site layout planning. Safety Science, 104, 246–259.

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.

Patel, D. A., & Jha, K. N. (2014). Neural network approach for safety climate prediction. Journal of Management in Engineering, 31(6), UNSP 05014027.

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.

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.).

Reason, J., (1990). Human error. Cambridge University Press.

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.

RStudio. (2019). Integrated development environment for R. (Computer Software).

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.

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.

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.

Stiles, S., Ryan, B., & Golightly, D. (2018). Evaluating attitudes to safety leadership within rail construction projects. Safety Science, 110, 134–144.

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.

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.

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.

U.S Bureau of Labor Statistics (2017). Injuries, illnesses, and fatalities.

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.

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.

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.

Winge, S. & Albrechtsen, E. (2018). Accident types and barrier failures in the construction industry. Safety Science, 105, 158–166.

Winge, S., Albrechtsen, E., & Mostue, B. A. (2019). Causal factors and connections in construction accidents. Safety Science, 112, 130–141.

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.

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).

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.

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.

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).

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.