Fusion Model for Hazard Association Network Development: A Case in Elevator Installation and Maintenance

  • Pin-Chao Liao
  • Hainan Chen
  • Xiaowei LuoEmail author
Construction Management


This paper proposes a fusion model for developing a data-driven risk association network, based on historical inspection records. The fusion model first re-categorizes the hazards based on the similarity in their occurrence patterns. Second, spatial and temporal heterogeneity of the hazard occurrence is examined, after which site-specific records as outliers are removed from the database. Third, a structured learning approach is used to investigate the causal relations between safety risks and the weight of each relation is calculated based on the association rules. Finally, the causal relations and weightings are fused to form the hazard association network, based on which critical hazards can be identified for safety management strategy planning. Safety management for an elevator installation and maintenance is used as a domain to validate the proposed fusion model, which develops the hazard association network using a dataset with 110,698 safety inspection records on 25,729 sites (with elevator installation or maintenance) managed by an elevator company. Using the developed network, critical hazards on the sites are identified for proactive construction management.


safety inspection data mining real-time association rules hazard association network data fusion hazard pattern proactive safety 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Allan, N. and Davis, J. (2010). “Strategic risks — thinking about them differently.” Proceedings of the Institution of Civil Engineers–Civil Engineering, vol. 159, no. 6, pp. 10–14, DOI: 10.1680/cien.2006. 159.6.10.Google Scholar
  2. Amiri, M., Ardeshir, A., Fazel Zarandi, M. H., and Soltanaghaei, E. (2016). “Pattern extraction for high–risk accidents in the construction industry: A data–mining approach.” International Journal of Injury Control and Safety Promotion, vol. 23, no. 3, pp. 264–276, DOI: 10.1080/17457300.2015.1032979.Google Scholar
  3. Bellamy, L. J., Geyer, T. A. W., and Wilkinson, J. (2008). “Development of a functional model which integrates human factors, safety management systems and wider organisational issues.” Safety Science, vol. 46, no. 3, pp. 461–492, DOI: 10.1016/j.ssci.2006.08.019.Google Scholar
  4. Bond, S. D., Carlson, K. A., and Keeney, R. L. (2008). “Generating Objectives: Can decision makers articulate what they want?” Management Science, vol. 54, no. 1, pp. 56–70, DOI: 10.1287/mnsc.1070.0754.Google Scholar
  5. Burns, C. and Conchie, S. (2013). “Risk information source preferences in construction workers.” Employee Relations, vol. 36, no. 1, pp. 70–81, DOI: 10.1108/ER–06–2013–0060.Google Scholar
  6. Campbell, S. and Currie, G. (2006). “Against beck: In defence of risk analysis.” Philosophy of the Social Sciences, vol. 36, no. 2, pp. 149–172, DOI: 10.1177/0048393106287209.Google Scholar
  7. Chang, D. S. and Tsai, Y.C. (2014). “Investigating the long–term change of injury pattern on severity, accident types and sources of injury in Taiwan’s manufacturing sector between 1996 and 2012.” Safety Science, vol. 68, pp. 231–242, DOI: 10.1016/j.ssci.2014.04.005.Google Scholar
  8. Chawla, S. (2010). “Feature selection, association rules network and theory building.” The Fourth Workshop on Feature Selection in Data Mining, pp. 14–21.Google Scholar
  9. Chen, L. and Luo, H. (2014). “A BIM–based construction quality management model and its applications.” Automation in Construction, vol. 46, pp. 64–73, DOI: 10.1016/j.autcon.2014.05.009.Google Scholar
  10. Cheng, C.–W., Lin, C.–C., and Leu, S.–S. (2010). “Use of association rules to explore cause–effect relationships in occupational accidents in the Taiwan construction industry.” Safety Science, vol. 48, no. 4, pp. 436–444, DOI: 10.1016/j.ssci.2009.12.005.Google Scholar
  11. Choudhry, R., Fang, D., and Mohamed, S. (2007). “Developing a model of construction safety culture.” Journal of management in engineering, vol. 23, no. 4, pp. 207–212, DOI: 10.1061/(ASCE)0742–597X(2007)23.Google Scholar
  12. Ericson, C. A. (2005). Hazard analysis techniques for system safety, 2nd Edition, Wiley, Hoboken, NJ, USA.Google Scholar
  13. Fang, C., Marle, F., Zio, E., and Bocquet, J. (2012). “Network theorybased analysis of risk interactions in large engineering projects.” Reliability Engineering & System Safety, vol. 106, pp. 1–10, DOI: 10.1016/j.ress.2012.04.005.Google Scholar
  14. Hallowell, M.R. and Gambatese, J.A. (2009). “Activity–based safety risk quantification for concrete formwork construction.” Journal of Construction Engineering and Management, vol. 135, no. 10, pp. 990–998, doi: 10.1061/(ASCE)CO.1943–7862.0000071.Google Scholar
  15. Ke, Y., Cheng, J., and Ng, W. (2008). “An information–theoretic approach to quantitative association rule mining.” Knowledge and Information Systems, vol. 16, no. 2, pp. 213–244, DOI: 10.1007/s10115–007–0104–4.Google Scholar
  16. Kopsida, M., Brilakis, I., and Vela, P. (2015). “A review of automated construction progress and inspection methods.” Proceedings of the 32nd CIB W78 Conference on Construction IT, CIB, Eindhoven, Netherlands, pp. 421–431.Google Scholar
  17. Laflamme, L. and Eilert–Petersson, E. (1998). “School–injury patterns: A tool for safety planning at the school and community levels,” Accident Analysis and Prevention, vol. 30, no. 2, pp. 277–283, DOI: 10.1016/S0001–4575(97)00085–7.Google Scholar
  18. Latorella, K. A. and Prabhu, P. V (2000). “A review of human error in aviation maintenance and inspection.” International Journal of Industrial Ergonomics, vol. 26, no. 2, pp. 133–161, DOI: 10.1016/S0169–8141(99)00063–3.Google Scholar
  19. Leveson, N. (2004). “A new accident model for engineering safer systems.” Safety Science, vol. 42, no. 4, pp. 237–270, DOI: 10.1016/S0925–7535(03)00047–X.Google Scholar
  20. Mohamed, S. (2003). “Scorecard approach to benchmarking organizational safety culture in construction.” Journal of Construction Engineering and Management, vol. 129, no. 1, pp. 80–88, DOI: 10.1061/(ASCE)0733–9364(2003)129:1(80).MathSciNetGoogle Scholar
  21. Montibeller, G. and von Winterfeldt, D. (2015). “Cognitive and motivational biases in decision and risk analysis.” Risk Analysis, vol. 35, no. 7, pp. 1230–1251, DOI: 10.1111/risa.12360.Google Scholar
  22. Montoya–García, M. E., Callejón–Ferre, A. J., Pérez–Alonso, J., and Sánchez–Hermosilla, J. (2013). “Assessment of psychosocial risks faced by workers in Almería–type greenhouses, using the Mini Psychosocial Factor method.” Applied Ergonomics, vol. 44, no. 2, pp. 303–311, DOI: 10.1016/j.apergo.2012.08.005.Google Scholar
  23. Mosleh, A., Bier, V. M., and Apostolakis, G. (1988). “A critique of current practice for the use of expert opinions in probabilistic risk assessment.” Reliability Engineering and System Safety, vol. 20, no. 1, pp. 63–85, DOI: 10.1016/0951–8320(88)90006–3.Google Scholar
  24. Nahrgang, J. D., Morgeson, F. P., and Hofmann, D. A. (2011). “Safety at work: A meta–analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes.” Journal of Applied Psychology, vol. 96, no. 1, pp. 71–94, DOI: 10.1037/a0021484.Google Scholar
  25. Ng, S. T., Cheng, K. P., and Skitmore, R. M. (2005). “A framework for evaluating the safety performance of construction contractors.” Building and Environment, vol. 40, no. 10, pp. 1347–1355, DOI: 10.1016/j.buildenv.2004.11.025.Google Scholar
  26. O’Brien, W. J., Julien, C. L., Kabadayi, S., Luo, X., and Hammer, J. (2009). “An architecture for decision support in ad hoc sensor networks.” ITcon, vol. 14, pp. 309–327.Google Scholar
  27. OSHA (2015). Fatality and Catastrophe Investigation Summaries.Google Scholar
  28. Page, L., Brin, S., Motwani, R., and Winograd, T. (1998). “The pagerank citation ranking: Bringing order to the web.” Proceedings of the 7th International World Wide Web Conference, Brisbane, Australia, pp. 161–172.Google Scholar
  29. Park, C.–S. and Kim, H.–J. (2013). “A framework for construction safety management and visualization system.” Automation in Construction, vol. 33, pp. 95–103, DOI: 10.1016/j.autcon.2012.09.012.Google Scholar
  30. Park, J., Kim, K., and Cho, Y. K. (2017). “Framework of automated construction–safety monitoring using cloud–enabled BIM and BLE mobile tracking sensors.” Journal of Construction Engineering and Management, Vol. 143, No. 2, 05016019, DOI: 10.1061/(ASCE) CO.1943–7862.0001223.Google Scholar
  31. Renuka1, S. M., Umarani, C., and Kamal, S. (2014). “A review on critical risk factors in the life cycle of construction projects.” Journal of Civil Engineering Research, Vol. 4, No. 2A, pp. 31–36, DOI: 10.5923/c.jce.201401.07.Google Scholar
  32. Steinhaeuser, K., Chawla, N. V., and Ganguly, A. R. (2011). “Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science.” Statistical Analysis and Data Mining, vol. 4, no. 5, pp. 497–511, DOI: 10.1002/sam.10100.MathSciNetGoogle Scholar
  33. Suraji, A., Duff, A. R., and Peckitt, S. J. (2001). “Development of causal model of construction accident causation.” Journal of Construction Engineering and Management, vol. 127, no. 4, pp. 337–344, DOI: 10.1227/01.NEU.0000210260.55124.A4.Google Scholar
  34. Tan, P.–N., Steinbach, M., and Kumar, V. (2005). Introduction to data mining, 1st Edition, Pearson, Boston, MA, USA.Google Scholar
  35. Yang, R. J. and Zou, P. X. W. (2014). “Stakeholder–associated risks and their interactions in complex green building projects: A social network model.” Building and Environment, vol. 73, pp. 208–222, DOI: 10.1016/j.buildenv.2013.12.014.Google Scholar
  36. Ye, Y. and Chiang, C. C. (2006). “A parallel apriori algorithm for frequent itemsets mining.” Fourth International Conference on Software Engineering Research, Management and Applications (SERA’06), IEEE, pp. 87–94.Google Scholar
  37. Wang, T., Fang, D., and Li, G. (2015). “Innovated safety inspection system on construction site based on mobile phone application.” International Symposium on Computers & Informatics (ISCI 2015), Atlantis Press, Beijing, China, pp. 625–632.Google Scholar
  38. Zhang, X. (2005). “Criteria for selecting the private–sector partner in public–private partnerships.” Journal of Construction Engineering and Management, vol. 131, no. 6, pp. 631–644, DOI: 10.1061/(ASCE)0733–9364(2005)131:6(631).Google Scholar
  39. Zhang, S., Boukamp, F., and Teizer, J. (2015). “Ontology–based semantic modeling of construction safety knowledge: Towards automated safety planning for Job Hazard Analysis (JHA).” Automation in Construction, vol. 52, pp. 29–41, DOI: 10.1016/j.autcon.2015.02.005.Google Scholar
  40. Zhao, D., McCoy, A. P., Kleiner, B. M., Smith–Jackson, T. L., and Liu, G. (2016). “Sociotechnical systems of fatal electrical injuries in the construction industry.” Journal of Construction Engineering and Management, Vol. 142, No. 1, p. 04015056, DOI: 10.1061/(ASCE) CO.1943–7862.0001036.Google Scholar
  41. Zou, P. X. W. and Zhang, G. (2009). “Managing risks in construction projects: Life cycle and stakeholder perspectives.” International Journal of Construction Management, vol. 9, no. 1, pp. 61–77, DOI: 10.1080/15623599.2009.10773122.Google Scholar
  42. Zou, P. X. W., Zhang, G., and Wang, J. (2007). “Understanding the key risks in construction projects in China.” International Journal of Project Management, vol. 25, no. 6, pp. 601–614, DOI: 10.1016/j.ijproman.2007.03.001.Google Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dept. of Construction Management, School of Civil EngineeringTsinghua UniversityBeijingChina
  2. 2.Dept. of Architecture and Civil EngineeringCity University of Hong KongKowloon TongHong Kong

Personalised recommendations