Skip to main content
SpringerLink
Log in

An Approach to Explore Historical Construction Accident Data Using Data Mining Techniques

  • Conference paper
  • First Online:
Advances in Engineering Networks (ICIEOM 2018)

Part of the book series: Lecture Notes in Management and Industrial Engineering ((LNMIE))

  • 351 Accesses

Abstract

Construction worksites are characterized by their dynamic and complex nature, making that work safety awareness a major concern during the project life cycle. In this regard, the analysis of historical data might be useful to identify the most frequent relationship between the variables of accidents in order to help safety practitioners in the task of prioritizing preventive actions. In this work, we propose an approach that will allow to explore unknown relations, expressed as association rules, among diverse variables from a database of construction accidents’ data. These association rules may be useful for efficient safety prevention and control.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Antony Chettupuzha AJ, Haas CT (2015) Algorithm for determining the criticality of documents within a construction information system. J Comput Civ Eng 30(3):04015039

    Article  Google Scholar 

  2. Arquillos AL, Romero JCR, Gibb A (2012) Analysis of construction accidents in Spain, 2003–2008. J Saf Res 43(5–6):381–388

    Article  Google Scholar 

  3. Bansal V (2011) Application of geographic information systems in construction safety planning. Int J Project Manage 29(1):66–77

    Article  Google Scholar 

  4. Berry MJ, Linoff G (1997) Data mining techniques: for marketing, sales, and customer support. Wiley

    Google Scholar 

  5. Boraiko C, Beardsley T, Wright E (2008) Accident investigations one element of an effective safety culture. Prof Saf 53(09)

    Google Scholar 

  6. Camino López MA, Ritzel DO, Fontaneda I, González Alcantara OJ (2008) Construction industry accidents in spain. J Saf Res 39(5):497–507

    Article  Google Scholar 

  7. Carrillo-Castrillo JA, Trillo-Cabello AF, Rubio-Romero JC (2017) Construction accidents: identification of the main associations between causes, mechanisms and stages of the construction process. Int J Occup Saf Ergon 23(2):240–250

    Article  Google Scholar 

  8. Cheng CW, Leu SS, Cheng YM, Wu TC, Lin CC (2012) Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan’s construction industry. Accid Anal Prev 48:214–222. https://doi.org/10.1016/j.aap.2011.04.014

    Article  Google Scholar 

  9. Chi CF, Chang TC, Ting HI (2005) Accident patterns and prevention measures for fatal occupational falls in the construction industry. Appl Ergon 36(4):391–400

    Article  Google Scholar 

  10. Chi S, Han S, Kim DY (2013) The relationship between unsafe working conditions and workers: Behaviour and impact of working conditions on injury severity in U.S. construction industry. J Constr Eng Manag 139:826–838

    Article  Google Scholar 

  11. Chi S, Suk SJ, Kang Y, Mulva SP (2012) Development of a data mining-based analysis framework for multi-attribute construction project information. Adv Eng Inform 26(3):574–581

    Article  Google Scholar 

  12. Comunicación de la comisión al parlamento europeo, al consejo, al comité económico y social europeo y al comité de las regiones (2017) Trabajo más seguro y saludable para todos - Modernización de la legislación y las políticas de la UE de salud y seguridad en el trabajo. Bruselas, 10.1.2017

    Google Scholar 

  13. Eurostat (2001) European statistics on accidents at work. Methodology- 2001 edition. https://doi.org/10.2785/40882

  14. EU-OSHA (2017) An international comparison of the cost of work-related accidents and illnesses. Publications Office of the European Union, Luxembourg. https://osha.europa.eu/en/tools-and-publications/publications/international-comparison-cost-work-related-accidents-and/view

  15. Fan H, Li H (2013) Retrieving similar cases for alternative dispute resolution in construction accidents using text mining techniques. Autom Constr 34(1):85–91

    Article  Google Scholar 

  16. Guo H, Li H, Li V (2013) VP-based safety management in large-scale construction projects: a conceptual framework. Autom Constr 34(1):16–24

    Article  Google Scholar 

  17. Harms-Ringdahl L (2004) Relationships between accident investigations, risk analysis, and safety management. J Hazard Mater 111(1–3):13–19

    Article  Google Scholar 

  18. Hoła B, Szóstak M (2017) An occupational profile of people injured in accidents at work in the polish construction Industry. Procedia Eng 208:43–51

    Article  Google Scholar 

  19. Khanzode VV, Maiti J, Ray P (2012) Occupational injury and accident research: a comprehensive review. Saf Sci 50(5):1355–1367

    Article  Google Scholar 

  20. Kim H, Lee H-S, Park M, Chung B, Hwang S (2013) Information retrieval framework for hazard identification in construction. J Comput Civ Eng 04014052. https://doi.org/10.1061/(asce)cp.1943-5487.0000340

  21. Liew MP, Rosenblatt J (2003) Using data mining techniques for improving building life cycle

    Google Scholar 

  22. Martínez-Rojas M, Marín N, Molina C, Vila MA (2016) An intelligent system for cost data handling in construction projects. In: 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 359–366

    Google Scholar 

  23. Martínez-Rojas M, Marín N, Vila MA (2015) The role of information technologies to address data handling in construction project management. J Comput Civ Eng 30(4):04015064

    Article  Google Scholar 

  24. Martínez-Rojas M, Pardo Ferreira MC, López-Arquillos A, Rubio-Romero JC (2017) A preliminary approach for accident analysis in construction industry using the multidimensional model. In: International symposium on occupational safety and hygiene, Guimaraes, Portugal

    Google Scholar 

  25. Martínez-Aires MD, López-Alonso M, Martínez-Rojas M (2018) Building information modeling and safety management: a systematic review. Saf Sci 101:11–18

    Article  Google Scholar 

  26. Ministerio de Empleo y Seguridad Social (2017) http://www.empleo.gob.es/index.htm

  27. Papadopoulos G, Georgiadou P, Papazoglou C, Michaliou K (2010) Occupational and public health and safety in a changing work environment: an integrated approach for risk assessment and prevention. Saf Sci 48(8):943–949

    Article  Google Scholar 

  28. Pillay M (2015) Accident causation, prevention and safety management: a review of the state-of-the-art. Procedia Manuf 3:1838–1845

    Article  Google Scholar 

  29. Rivas T, Paz M, Martín J, Matías J, García J, Taboada J (2011) Explaining and predicting workplace accidents using data-mining techniques. Reliab Eng Syst Saf 96(7):739–747

    Article  Google Scholar 

  30. Shin DP, Park YJ, Seo J, Lee DE (2017) Association rules mined from construction accident data. KSCE J Civ Eng 1–13

    Google Scholar 

  31. Soibelman L, Hyunjoo K (2000) Generating construction knowledge with knowledge discovery in databases. Comput Civ Build Eng 2(3):914–921

    Google Scholar 

  32. Tixier AJP, Hallowell MR, Rajagopalan B, Bowman D (2017) Construction safety clash detection: identifying safety incompatibilities among fundamental attributes using data mining. Autom Constr 74:39–54

    Article  Google Scholar 

  33. Xia N, Wanga X, Griffin MA, Chunlin W, Liu B (2017) Do we see how they perceive risk? An integrated analysis of risk perception and its effect on workplace safety behavior. Accid Anal Prev 106:234–242

    Article  Google Scholar 

Download references

Acknowledgements

This work has been partially supported by the Spanish Ministry of Economic, Industry and Competitiveness for financing project BIA2016-79270-P and the postdoctoral program (FJCI-2015-24093). It is also supported by the Ministry of Education, Culture and Sports of the Government of Spain for the predoctoral contracts “Formación del Profesorado Universitario” (FPU 2016/03298).

Author information

Authors and Affiliations

  1. Dpto. de Economía y Administración de Empresas, Escuela de Ingenierías Industriales, Universidad de Málaga, C/Dr, Ortiz Ramos S/N, 29071, Málaga, Spain

    María Martínez Rojas, Mª del Carmen Pardo Ferreira & Juan Carlos Rubio Romero

  2. Universidad de Málaga, C/Dr, Ortiz Ramos S/N, 29071, Málaga, Spain

    Antonio Trillo Cabello

Authors
  1. María Martínez Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonio Trillo Cabello
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mª del Carmen Pardo Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan Carlos Rubio Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to María Martínez Rojas .

Editor information

Editors and Affiliations

  1. Department of Business Administration, Management and Product Design, Universitat de Girona, Girona, Spain

    Rodolfo de Castro

  2. Department of Business Administration, Management and Product Design, Universitat de Girona, Girona, Spain

    Gerusa Giménez

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Martínez Rojas, M., Trillo Cabello, A., Pardo Ferreira, M.d., Rubio Romero, J.C. (2020). An Approach to Explore Historical Construction Accident Data Using Data Mining Techniques. In: de Castro, R., Giménez, G. (eds) Advances in Engineering Networks. ICIEOM 2018. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-44530-0_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-44530-0_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44529-4

  • Online ISBN: 978-3-030-44530-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation