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ETD Abstract Container

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Using a corpus of accidents to reveal adaptive patterns that threaten safety

Walker, Katherine E
http://rave.ohiolink.edu/etdc/view?acc_num=osu1610024273554285

Abstract Details

2021, Doctor of Philosophy, Ohio State University, Industrial and Systems Engineering.
A finding in safety research is that accidents with the same characteristics are happening over and over, both within and across domains. After dramatic system failures, stakeholders are clearly having trouble learning from accidents to anticipate future challenges to the safety of their systems. Difficulties in learning from accidents is a concern within domains, but especially across domains, where variations in the surface characteristics of accidents can prevent stakeholders from visualizing how a similar accident could happen in their system. The question becomes: how can systems effectively learn from accidents in a way that helps them to anticipate future challenges, both within their domain and outside it? Facilitating learning from accidents to help anticipate future challenges across domains requires abstracting away from the surface characteristics of the accident to discover patterns of how complex systems become brittle and fail. Drawing conclusions from only one case risks finding patterns that adhere too closely to the idiosyncrasies of that case. As a result, the best way to discover general patterns of how complex systems fail is to perform an analysis across a corpus of cases. The patterns resulting from those meta-analyses can be used to create retrospective indicators of when complex systems lost adaptive capacity and moved toward failure. Those indicators can potentially help systems anticipate future disturbances and changes to adaptive capacity. The first objective of this dissertation is to develop and use the Systemic Contributors and Adaptations (SCA) method as a systemic method for analyzing accidents. SCA is designed as a lightweight method for analyzing accidents to facilitate the creation of a corpus of cases to inform a meta-analysis. SCA uses pressures, goal conflicts, and resulting adaptations to trace how adaptive capacity changes throughout an accident. The study aimed at testing SCA as a lightweight method and understanding if the output produced helped to discover recurring relationships between the accidents. The second objective is to identify general patterns between the accidents based on a meta-analysis across the corpus of accidents. The patterns of adaptation cut across accidents from different domains and with different specific failure mechanisms. Each pattern is a result of contrasting the SCA analyses of two or more accidents and discovering recurring relationships. The patterns describe general patterns in how specific sets of pressures and goal conflicts result in maladaptations that reliably move the system closer to failure. The third objective is to identify prospective indicators of adaptations that decrease resilience. The patterns of adaptation are used as the basis for developing indicators that systems are losing adaptive capacity and becoming brittle to future disturbances. To be considered prospective, any indicator created must differentiate between a system performing adaptations that preserve safety margins, and a system that is beginning to degrade. These indicators, created from accidents, allow systems to anticipate the impacts of upcoming disturbances and challenges.
Michael Rayo (Advisor)
David Woods (Committee Member)
Emily Patterson (Committee Member)
351 p.
Industrial Engineering
Accident investigation; Safety; SCA; SCAD;

Recommended Citations

Citations

  • Walker, K. E. (2021). Using a corpus of accidents to reveal adaptive patterns that threaten safety [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1610024273554285

    APA Style (7th edition)

  • Walker, Katherine. Using a corpus of accidents to reveal adaptive patterns that threaten safety. 2021. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1610024273554285.

    MLA Style (8th edition)

  • Walker, Katherine. "Using a corpus of accidents to reveal adaptive patterns that threaten safety." Doctoral dissertation, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1610024273554285

    Chicago Manual of Style (17th edition)

osu1610024273554285
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© 2021, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.