Safety Decision-Making in Academia

  • Anastasia Kalugina
  • Thierry MeyerEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1204)


This paper illustrates the challenges of safety management in Academia, which are difficult to overcome using conventional methods of risk management. The positive aspects and disadvantages of the risk management tools specifically designed for the laboratory collection are addressed. We propose a different approach to hazard detection that can ease the burden for Academia safety experts of tedious time-consuming risk assessment. Based on the observations of Academia’s safety management, we are attempting to build a semi-quantitative risk analysis model that will conform to the concept of safety-II. This paper suggests an approach that allows to integrate this model into human oriented performance driven decision making.


Safety-II Risk management Safety climate Academia 


  1. 1.
    Hollnagel, E.: Safety-I and safety-II. Ashgate Publishing Limited, Farnham (2014)Google Scholar
  2. 2.
    Centemeri, L.: The Seveso disaster legacy. In: Nature and History in Modern Italy, pp. 251–273. Ohio University Press & Swallow Press (2010)Google Scholar
  3. 3.
  4. 4.
    Khan, A.H., Hasan, S., Sarkar, M.A.R.: Analysis of possible causes of Fukushima disaster. Int. J. Nucl. Quantum Eng. 12(2), 53–58 (2018)Google Scholar
  5. 5.
  6. 6.
  7. 7.
    Bilir, S., Gürcanli, G.: A method for determination of accident probability in construction industry. Teknik Dergi 29(4), 8537–8561 (2018)Google Scholar
  8. 8.
  9. 9.
  10. 10.
    Ludwig, J., Bastin, G., Chewings, V., Eager, R., Liedloff, A.: Leakiness: a new index for monitoring the health of arid and semiarid landscapes using remotely sensed vegetation cover and elevation data. Ecol. Ind. 7, 442–454 (2007)CrossRefGoogle Scholar
  11. 11.
  12. 12.
  13. 13. Accessed 08 Dec 2019
  14. 14.
  15. 15.
  16. 16.
    Viner, D.: Accident analysis and risk control. VHMS, Fairfield (1994)Google Scholar
  17. 17.
    The core body of knowledge for generalist OHS professionals. Safety Institute of Australia, Tullamarine (2012)Google Scholar
  18. 18.
    Macdonald, W.: A hierarchy of risk control measures for prevention of work-related musculoskeletal disorders. In: International Ergonomics Conference on Humanising Work and the Work Environment, Guwahati, India (2005)Google Scholar
  19. 19.
    Marendaz, J., Suard, J., Meyer, T.: A systematic tool for assessment and classification of hazards in laboratories (ACHiL). Saf. Sci. 53, 168–176 (2013)CrossRefGoogle Scholar
  20. 20.
    Valis, D., Koucky, M.: Selected overview of risk assessment techniques. Problemy ekspoatacji, 19–23 (2009)Google Scholar
  21. 21.
    Hashemi-Tilehnoee, M., Pazirandeh, A., Tashakor, S.: HAZOP-study on heavy water research reactor primary cooling system. Ann. Nucl. Energy 37, 428–433 (2010)CrossRefGoogle Scholar
  22. 22.
    Rozenfeld, O., Sacks, R., Rosenfeld, Y., Baum, H.: Construction job safety analysis. Saf. Sci. 48, 491–498 (2010)CrossRefGoogle Scholar
  23. 23.
    Ericson, C.: Hazard Analysis Techniques for System Safety. Wiley-Interscience, Hoboken (2010)Google Scholar
  24. 24.
    Guimarães, A., Lapa, C.: Fuzzy inference to risk assessment on nuclear engineering systems. Appl. Soft Comput. 7, 17–28 (2007)CrossRefGoogle Scholar
  25. 25.
    Díaz, R., Fernánde, G., Muzzio, C.: Practical application of quality risk management to the filling process of betamethasone injections. Pharm. Eng. 31, 84–89 (2011)Google Scholar
  26. 26.
    Dai, W., Maropoulos, P., Cheung, W., Tang, X.: Decision-making in product quality based on failure knowledge. Int. J. Prod. Lifecycle Manag. 5, 143 (2011)CrossRefGoogle Scholar
  27. 27.
    Park, A., Lee, S.: Fault tree analysis on handwashing for hygiene management. Food Control 20, 223–229 (2009)CrossRefGoogle Scholar
  28. 28.
    You, X., Tonon, F.: Event-tree analysis with imprecise probabilities. Risk Anal. 32, 330–344 (2011)CrossRefGoogle Scholar
  29. 29.
    Givehchi, S., Heidari, A.: Bayes networks and fault tree analysis application in reliability estimation (case study: automatic water sprinkler system). Environ. Energy Econ. Res. 2(4), 325–341 (2018)Google Scholar
  30. 30.
    Leggett, D.: Lab-HIRA: hazard identification and risk analysis for the chemical research laboratory: part 1. Preliminary hazard evaluation. J. Chem. Health Saf. 19, 9–24 (2012)CrossRefGoogle Scholar
  31. 31.
    Ouédraogo, A., Groso, A., Meyer, T.: Risk analysis in research environment – part I: modeling lab criticity index using improved risk priority number. Saf. Sci. 49, 778–784 (2011)CrossRefGoogle Scholar
  32. 32.
    Kletz, T.A.: Looking beyond ALARP: overcoming its limitations. Process Saf. Environ. Prot. 83, 81–84 (2005)CrossRefGoogle Scholar
  33. 33.
    Kron, H.H.: On the evaluation of risk acceptance principles.
  34. 34.
    Wigger, P.: Experience with safety integrity level (SIL) allocation in railway applications (2001)Google Scholar
  35. 35.
    Gosman, S.: Justifying safety: the paradox of rationality. 90 Temp. L. Rev. 155 (2018). SSRN.
  36. 36.
    Leimeister, M., Kolios, A.: A review of reliability-based methods for risk analysis and their application in the offshore wind industry. Renew. Sustain. Energy Rev. 91, 1065–1076 (2018)CrossRefGoogle Scholar
  37. 37.
    Herrmann, J.: Engineering Decision Making and Risk Management. Wiley, New York (2015)zbMATHGoogle Scholar
  38. 38.
    Eggemeier, F.: Properties of workload assessment techniques. Adv. Psychol. 52, 41–62 (1988)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Group of Physical and Chemical Safety, Institute of Chemical Sciences and EngineeringEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Safety Competence CenterEcole Polytechnique Fédérale de LausanneLausanneSwitzerland

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