Skip to main content
SpringerLink
Log in
Book cover

Workshop of the European Group for Intelligent Computing in Engineering

EG-ICE 2018: Advanced Computing Strategies for Engineering pp 359–384Cite as

Quantitative Analysis of Close Call Events

  • Conference paper
  • First Online:

  • 3026 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

Abstract

Construction safety is a big problem according to official statistics. In many of the developed countries about 15–25% of all fatal construction workplace accidents relate to too close proximity of pedestrian workers to construction equipment or hazardous materials. Extracting knowledge from data to near hits (aka. close calls) might warrant better understanding on the root causes that lead to such incidents and eliminate them. While a close call is a subtle event where workers are in close proximity to a hazard, its frequency depends – amongst other factors – on poor site layout, a worker’s willingness to take risks, limited safety education, and pure coincidence. Some pioneering organizations have recognized the potential on gathering and analyzing leading indicator data on close calls. However, mostly manual approaches are infrequently performed, subjective due to situational assessment, imprecise in level of detail, and importantly, reactive or inconsistent in effective or timely follow-ups by management. While existing predictive analytics research targets change at strategic levels in the hierarchy of organizations, personalized feedback to strengthen an individual worker’s hazard recognition and avoidance skill set is yet missing. This study tackles the bottom of Heinrich’s safety pyramid by providing an in-depth quantitative analysis of close calls. Modern positioning technology records the trajectory data of personnel, equipment, and materials. Computational algorithms then automatically generate previously unavailable details to close call events. The derived information is embedded in simplified geometric information models that users on a construction site can retrieve, easily understand, and adapt in existing preventative hazard recognition and control processes. Results from scientific and field experiments demonstrate that the developed system works successfully under the constraints of currently available positioning technology.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. Bird, F.E., Germain, G.L., Bird Jr., F.E.: Practical Loss Control Leadership. Revised edn. Intl. Loss Control Inst. (1996). ISBN-13: 978-0880610544

    Google Scholar 

  2. Cavalieri, S., Ghislandi, W.M.: A conceptual structure for the use of near-misses properties. IFAC 39(3), 81–86 (2006)

    Google Scholar 

  3. NIOSH homepage. https://www.cdc.gov/niosh/face/inhouse.html. Accessed 20 Dec 2017

  4. Teizer, J.: Right-time vs. real-time pro-active construction safety and health system architecture. Constr. Innov. Inf. Process Manag. 16(3), 253–280 (2016)

    Article  Google Scholar 

  5. Marks, E., Teizer, J.: Method for testing proximity detection and alert technology for safe construction equipment operation, construction management and economics. Occup. Health Saf. Constr. Ind. 31(6), 636–646 (2013). http://www.tandfonline.com/doi/abs/10.1080/01446193.2013.783705. Taylor & Francis, Special Issue

    Google Scholar 

  6. CII: Near Miss Reporting to Enhance Safety Performance, The University of Texas at Austin (2014)

    Google Scholar 

  7. Cambraia, F.B., Saurin, T.A., Formoso, C.T.: Identification, analysis and dissemination on near misses: a case study in the construction industry. Saf. Sci. 48, 91–99 (2010)

    Article  Google Scholar 

  8. Heinrich, H.W.: Industrial Accident Prevention: A Scientific Approach. McGraw-Hill, New York (1931)

    Google Scholar 

  9. Smallwood, J., Emunze, F.: Towards zero fatalities, injuries and disease in construction. Procedia Eng. 164, 453–460 (2016)

    Article  Google Scholar 

  10. Gnoni, M.G., Lettera, G.: Near-miss management systems: a methodological comparison. J. Loss Prev. Process Ind. 25, 609–616 (2012)

    Article  Google Scholar 

  11. Gnoni, M.G., Saleh, J.H.: Near-miss management systems and observability-in-depth: handling safety incidents and accident precursors in light of safety principles. Saf. Sci. 91, 154–167 (2017)

    Article  Google Scholar 

  12. Raviv, G., Fishbain, B., Shapira, A.: Analyzing risk factors in crane-related near-miss and accident reports. Saf. Sci. 91, 192–205 (2017)

    Article  Google Scholar 

  13. Teizer, J., Cheng, T.: Proximity hazard indicator for workers-on-foot near miss interactions with construction equipment and geo-referenced hazard areas. Autom. Constr. 60, 58–73 (2015)

    Article  Google Scholar 

  14. Lu, M., Cheung, C.M., Li, H., Hsu, S.: Understanding the relationship between safety investment and safety performance of construction projects through agent-based modeling. Accid. Anal. Prev. 94, 8–17 (2016)

    Article  Google Scholar 

  15. Li, H., Lu, M., Hsu, S.-C., Gray, M., Huang, T.: Proactive behavior-based safety management for construction safety improvement. Saf. Sci. 75, 107–117 (2015)

    Article  Google Scholar 

  16. Hilfert, T., Teizer, J., König, M.: First person virtual reality for evaluation and learning of construction site safety. In: 33rd International Symposium on Automation and Robotics in Construction, Auburn, Alabama, USA (2016)

    Google Scholar 

  17. Wu, W., Yang, H., Chew, D.A.S., Yang, S., Gibb, A.G.F., Li, Q.: Towards an autonomous real-time tracking system of near-miss accidents on construction sites. Autom. Constr. 19, 134–141 (2010)

    Article  Google Scholar 

  18. Teizer, J., Cheng, T., Fang, Y.: Location tracking and data visualization technology to advance construction ironworkers’ education and training in safety and productivity. Autom. Constr. 35, 53–68 (2013)

    Article  Google Scholar 

  19. Rozenfeld, O., Sacks, R., Rosenfeld, Y., Baum, H.: Construction job safety analysis. Saf. Sci. 48, 491–498 (2010)

    Article  Google Scholar 

  20. Zhang, S., Teizer, J., Lee, J.-K., Eastman, C., Venugopal, M.: Building information modeling (BIM) and safety: automatic safety checking of construction models and schedules. Autom. Constr. 29, 183–195 (2013)

    Article  Google Scholar 

  21. Zhang, S., Boukamp, F., Teizer, J.: Ontology-based semantic modeling of construction safety knowledge: towards automated safety planning for job hazard analysis (JHA). Autom. Constr. 52, 29–41 (2015)

    Article  Google Scholar 

  22. Lu, Y., Li, Q., Zhou, Z., Deng, Y.: Ontology-based knowledge modeling for automated construction safety checking. Saf. Sci. 79, 11–18 (2015)

    Article  Google Scholar 

  23. Schwabe, K., König, M., Teizer, J.: BIM applications of rule-based checking in construction site layout planning tasks. 33rd International Symposium on Automation and Robotics in Construction, Auburn, Alabama, USA (2016)

    Google Scholar 

  24. Zhang, S., Teizer, J., Pradhananga, N., Eastman, C.M.: Workforce location tracking to model, visualize and analyze workspace requirements in building information models for construction safety planning. Autom. Constr. 60, 74–86 (2015)

    Article  Google Scholar 

  25. Golovina, O., Teizer, J., Pradhananga, N.: Heat map generation for predictive safety planning: preventing struck-by and near miss interactions between workers-on-foot and construction equipment. Autom. Constr. 71, 99–115 (2016)

    Article  Google Scholar 

  26. Pradhananga, N., Teizer, J.: Automatic spatiotemporal analysis of construction site equipment operations using GPS data. Autom. Constr. 29, 107–122 (2013)

    Article  Google Scholar 

  27. Vasconcelos, B., Barkokébas Jr., B.: The causes of work place accidents and their relation to construction equipment design. Procedia Manuf. 3, 4392–4399 (2015)

    Article  Google Scholar 

  28. Hinze, J.W., Teizer, J.: Visibility-related fatalities related to construction equipment. Saf. Sci. 49, 709–718 (2011)

    Article  Google Scholar 

  29. Ranney, J.M., Zuschlag, M.K., Morell, J., Coplen, M.K., Multer, J., Raslear, T.G.: Evaluations of demonstration pilots produce change: fourteen years of safety-culture improvement efforts by the federal railroad administration. TR News – Railroads Res. Shar. Track 286, 28–36 (2013)

    Google Scholar 

  30. Hallowell, M.R., Hinze, J.W., Baud, K.C., Wehle, A.: Proactive construction safety control: measuring, monitoring, and responding to safety leading indicators. J. Constr. Eng. Manag. 139, 04013010 (2013)

    Article  Google Scholar 

  31. Cheng, T., Venugopal, M., Teizer, J., Vela, P.A.: Performance evaluation of ultra wideband technology for construction resource location tracking in harsh environments. Autom. Constr. 20(8), 1173–1184 (2011)

    Article  Google Scholar 

  32. Siebert, S., Teizer, J.: Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system. Autom. Constr. 41, 1–14 (2014). https://doi.org/10.1016/j.autcon.2014.01.004

    Article  Google Scholar 

  33. Kim, C., Haas, C.T., Liapi, K.A., McLaughlin, J., Teizer, J., Bosche, F.: Rapid human-assisted, obstacle avoidance system using sparse range point. In: Proceedings of the 9th Biennial ASCE Aerospace Division International Conference on Engineering, Construction, and Operations in Challenging Environments, League City, Houston, Texas, pp. 115–122 (2004)

    Google Scholar 

  34. Teizer, J.: Safety 360: surround-view sensing to comply with changes to the ISO 5006 earth-moving machinery - operator’s field of view - test method and performance criteria. In: Proceedings of the 32nd International Symposium on Automation and Robotics in Construction, Oulu, Finland (2015)

    Google Scholar 

  35. BG Bau homepage. http://www.bgbau-medien.de/struktur/inh_baus.htm. Accessed 30 Nov 2017

  36. OSHA homepage: Evaluation of what is considered a safe speed to operate a powered industrial truck, Occupational Safety and Health Administration. https://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=INTERPRETATIONS&p_id=24995. Accessed 30 Nov 2017

  37. Kamat, V.R., Martinez, J.C.: Visualizing simulated construction operations in 3D. J. Comput. Civ. Eng. 15(4), 329–337 (2001)

    Article  Google Scholar 

  38. Krepp, S., Jahr, K., Bigontina, S., Bügler, M., Borrmann, A.: BIMsite - towards a BIM-based generation and evaluation of realization variants comprising construction methods, site layouts and schedules. In: Proceedings of the EG-ICE Workshop on Intelligent Computing in Engineering, Krakow, Poland (2016)

    Google Scholar 

  39. Yang, J., Vela, P.A., Teizer, J., Shi, Z.K.: Vision-based crane tracking for understanding construction activity. ASCE J. Comput. Civ. Eng. 28(1), 103–112 (2014)

    Article  Google Scholar 

  40. Cheng, T., Teizer, J.: Modeling tower crane operator visibility to minimize the risk of limited situational awareness. ASCE J. Comput. Civ. Eng. 28(3), 04014004 (2014). http://ascelibrary.org/doi/abs/10.1061/(ASCE)CP.1943-5487.0000282

    Article  Google Scholar 

  41. Teizer, J., Allread, B.S., Fullerton, C.E., Hinze, J.: Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system. Autom. Constr. 19(5), 630–640 (2010). https://doi.org/10.1016/j.autcon.2010.02.009

    Article  Google Scholar 

  42. Luo, X., Li, H., Huang, T., Rose, T.: A field experiment of workers’ responses to proximity warnings of static safety hazards on construction sites. Saf. Sci. 84, 216–224 (2016)

    Article  Google Scholar 

  43. Yang, K., Ahn, C.R., Vuran, M.C., Aria, S.S.: Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit. Autom. Constr. 68, 194–202 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ruhr-University Bochum, Bochum, Germany

    Olga Golovina, Manuel Perschewski, Jochen Teizer & Markus König

Authors
  1. Olga Golovina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Perschewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jochen Teizer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Markus König
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jochen Teizer .

Editor information

Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Golovina, O., Perschewski, M., Teizer, J., König, M. (2018). Quantitative Analysis of Close Call Events. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91635-4_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91634-7

  • Online ISBN: 978-3-319-91635-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation