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Normative Visual Patterns for Hazard Recognition: A Crisp-Set Qualitative Comparative Analysis Approach

  • Construction Management
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Understanding the mental representations used for hazard recognition would help the development of inspection strategies for effective safety management. This study is part of ongoing research to identify hazard recognition patterns based on users’ mental representations. Hence, this study explored normative visual patterns for improving hazard recognition performance using a crisp-set qualitative comparative analysis (cs-QCA). Eye-tracking data and visual trajectories were collected using an eye-tracking device in a structural laboratory. A cs-QCA approach was adopted to analyze and summarize normative visual patterns that were used to successfully detect hazards by all participants, namely, potential electrical contact, a large machine with no guardrails, and steel bars dump. The results show that object identification should suffice as the basis for identifying electricity-related hazards, while struck-by hazards should focus on the objects and their pivot points or potential movement trajectories. The experimental design and analytical approach provide new insights into visual analytics in hazard recognition. The research extends and supplements recognition by component theory in the context of construction hazard recognition. The results also provide new and practical references for hazard inspection training, as well as for future development of automated hazard recognition systems.

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References

  • Albert A, Jeelani I, Han K, Azevedo R (2018) Are visual search patterns predictive of hazard recognition performance? Empirical investigation using eye-tracking technology. Journal of Construction Engineering and Management 145(1), DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001589

  • Anders Ericsson RP (2016) Peak: Secrets from the new science of expertise. Houghton Mifflin Harcourt, New York, NY, USA, 235–267

    Google Scholar 

  • Anderson JR (1978) Arguments concerning representations for mental imagery. Psychological Review 85(4):249–277, DOI: https://doi.org/10.1037/0033-295x.85.4.249

    Article  Google Scholar 

  • Biederman I (1987) Recognition-by-components: A theory of human image understanding. Psychological Review 94(2):115–147, DOI: https://doi.org/10.1037/0033-295X.94.2.115

    Article  Google Scholar 

  • Box-Steffensmeier MJ, Brady HE, Collier D (2006) The Oxford handbook of political methodology. Oxford University Press, New York, NY, USA, 124–126

    Google Scholar 

  • Buscher G, Cutrell E, Morris MR (2009) What do you see when you’re surfing?: Using eye tracking to predict salient regions of web pages. Proceedings of the 27th international conference on human factors in computing systems, April 4–9, Boston, MA, USA

  • Cristino F, Mathôt S, Theeuwes J, Gilchrist ID (2010) Scanmatch: A novel method for comparing fixation sequences. Behavior Research Methods 42(3):692–700, DOI: https://doi.org/10.3758/BRM.42.3.692

    Article  Google Scholar 

  • Posner MI (1980) Orienting of attention Quarterly Journal of Experimental Psychology 32(1):3–25, DOI: https://doi.org/10.1080/00335558008248231

    Article  MathSciNet  Google Scholar 

  • Dewhurst R, Nystrm M, Jarodzka H, Foulsham T, Johansson R, Holmqvist K (2012) It depends on how you look at it: Scanpath comparison in multiple dimensions with multimatch, a vector-based approach. Behavior Research Methods 44(4):1079–1100, DOI: https://doi.org/10.3758/s13428-012-0212-2

    Article  Google Scholar 

  • Dzeng R-J, Lin C-T, Fang Y-C (2016) Using eye-tracker to compare search patterns between experienced and novice workers for site hazard identification. Safety Science 82:56–67, DOI: https://doi.org/10.1016/j.ssci.2015.08.008

    Article  Google Scholar 

  • Egenhofer MJ, Herring JR Smith T, Park KK (1991) A framework for the definition of topological relationships and an algebraic approach to spatial reasoning within this framework. National Center for Geographic Information and Analysis NCGIA Technical Reports Deposit, Santa Barbara, CA, USA

  • Esmaeili B (2017) Measuring the impacts of safety knowledge on construction workers’ attentional allocation and hazard detection using remote eye-tracking technology. Journal of Management in Engineering 33(5), DOI: https://doi.org/10.1061/(ASCE)ME.1943-5479.0000526

  • Fainshmidt S, Witt MA, Aguilera RV, Verbeke A (2020) The contributions of qualitative comparative analysis (QCA) to international business research. Journal of International Business Studies 51(4):455–466, DOI: https://doi.org/10.1057/s41267-020-00313-1

    Article  Google Scholar 

  • Frazier L, Hoyer WJ (1992) Object recognition by component features: Are there age differences. Experimental Aging Research 18(1):9–14, DOI: https://doi.org/10.1080/03610739208253905

    Article  Google Scholar 

  • Gerring J (2011) Social science methodology: A unified framework. Cambridge University Press, Cambridge, UK, 197–217

    Book  Google Scholar 

  • Greckhamer T, Furnari S, Fiss PC, Aguilera RV (2018) Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strategic Organization 16(4): 482–495, DOI: https://doi.org/10.1177/1476127018786487

    Article  Google Scholar 

  • Grofman B, Schneider CQ (2009) An introduction to crisp set QCA, with a comparison to binary logistic regression. Political Research Quarterly 62(4):662–672, DOI: https://doi.org/10.1177/1065912909338464

    Article  Google Scholar 

  • Gugerty L (2011) Situation awareness in driving. In: Handbook of human factors for automated, connected, and intelligent vehicles. CRC Press, Boca Raton, FL, USA, 265–272

    Google Scholar 

  • Gupta A, Kembhavi A, Davis LS (2009) Observing human-object interactions: Using spatial and functional compatibility for recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence 31(10):1775, DOI: https://doi.org/10.1109/tpami.2009.83

    Article  Google Scholar 

  • Hadikusumo BHW, Rowlinson S (2002) Integration of virtually real construction model and design-for-safety-process database. Automation in Construction 11(5):501–509, DOI: https://doi.org/10.1016/s0926-5805(01)00061-9

    Article  Google Scholar 

  • Hasanzadeh S, Esmaeili B, Dodd MD (2018) Examining the relationship between construction workers’ visual attention and situation awareness under fall and tripping hazard conditions: Using mobile eye tracking. Journal of Construction Engineering & Management, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001516

  • Jacob RJK, Karn KS (2003) Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. North-Holland, Amsterdam, Netherlands, 573–605

    Google Scholar 

  • Jarodzka H, Holmqvist K, Nyström M (2010) A vector-based, multidimensional scanpath similarity measure. Proceedings of the 2010 symposium on eye-tracking research & applications, March 22–24, Austin, TX, USA

  • Josephson S, Holmes ME (2012) Visual attention to repeated internet images: Testing the scanpath theory on the world wide web. ETRA ′02: Proceedings of the 2002 symposium on eye tracking research & applications, March, New Orleans, LA, USA

  • Kraus S, Ribeiro-Soriano D, Schüssler M (2018) Fuzzy-set qualitative comparative analysis (fsQCA) in entrepreneurship and innovation research — The rise of a method. International Entrepreneurship & Management Journal 14:15–33, DOI: https://doi.org/10.1007/s11365-017-0461-8

    Article  Google Scholar 

  • Kübler S (2004) Memory-based parsing. John Benjamins, Amsterdam, Netherlands, 260–263

    Book  Google Scholar 

  • Liao PC, Guo Z, Tsai CH, Ding J (2018) Spatial-temporal interrelationships of safety risks with dynamic partition analysis: A mechanical installation case. KSCE Journal of Civil Engineering 22(5):1572–1583, DOI: https://doi.org/10.1007/s12205-017-1397-9

    Article  Google Scholar 

  • Marx A (2010) Crisp-set qualitative comparative analysis (csQCA) and model specification: Benchmarks for future csQCA applications. International Journal of Multiple Research Approaches 4(2):138–158, DOI: https://doi.org/10.5172/mra.2010.4.2.138

    Article  Google Scholar 

  • Menges R, Tamimi H, Kumar C, Walber T, Schaefer C, Staab S (2018) Enhanced representation of web pages for usability analysis with eye tracking. ETRA ′18: Proceedings of the 2018 ACM symposium on eye tracking research & applications, June 14–17, Warsaw, Poland

  • Noton D, Stark L (1971) Scanpaths in eye movements during pattern perception. Science 171(3968):308–311, DOI: https://doi.org/10.1126/science.171.3968.308

    Article  Google Scholar 

  • Ragin CC (1987) The comparative method: Moving beyond qualitative and quantitative strategies. University of California Press, Oakland, CA, USA

    Google Scholar 

  • Ragin CC (2008) Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press, Chicago, IL, USA

    Book  Google Scholar 

  • Redline CD, Census BOT, Lankford CP (2001) Eye-movement analysis: A new tool for evaluating the design of visually administered instruments (paper and web). Aapor conference, May 11–14, Montreal, Canada

  • Santella A, Decarlo D (2004) Robust clustering of eye movement recordings for quantification of visual interest. Eye Tracking Research & Application Symposium, 27–34, DOI: https://doi.org/10.1145/968363.968368

  • Schneider CQ, Wagemann C (2010) Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets. Comparative Sociology 9(3), DOI: https://doi.org/10.1163/156913210X12493538729793

  • Timberlake M (1989) Reviewed work: The comparative method: Moving beyond qualitative and quantitative strategies. By Charles c. Ragin. Oxford University Press, Oxford, UK, 827–829

    Google Scholar 

  • Verweij S, Gerrits LM (2013) Understanding and researching complexity with qualitative comparative analysis: Evaluating transportation infrastructure projects. Evaluation 19(1):40–55, DOI: https://doi.org/10.1177/1356389012470682

    Article  Google Scholar 

  • Xu Q, Chong H-Y, Liao P-C (2019) Exploring eye-tracking searching strategies for construction hazard recognition in a laboratory scene. Safety Science 120:824–832, DOI: https://doi.org/10.1016/j.ssci.2019.08.012

    Article  Google Scholar 

  • Yarbus AL (1967) Saccadic eye movements. In: Eye movements and vision. Springer, Boston, MA, USA, 129–146, DOI: https://doi.org/10.1007/978-14899-5379-7_5

    Chapter  Google Scholar 

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Authors and Affiliations

  1. School of Design and the Built Environment, Curtin University, Perth, WA, 6845, Australia

    Heap-Yih Chong

  2. Dept. of Construction Management, School of Civil Engineering, Tsinghua University, Beijing, 100084, China

    Mingxuan Liang & Pin-Chao Liao

Authors
  1. Heap-Yih Chong
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  2. Mingxuan Liang
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  3. Pin-Chao Liao
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Correspondence to Pin-Chao Liao.

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Chong, HY., Liang, M. & Liao, PC. Normative Visual Patterns for Hazard Recognition: A Crisp-Set Qualitative Comparative Analysis Approach. KSCE J Civ Eng 25, 1545–1554 (2021). https://doi.org/10.1007/s12205-021-1362-5

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  • DOI: https://doi.org/10.1007/s12205-021-1362-5

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