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Identifying route selection strategies in offshore emergency situations using decision trees

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  • Musharraf, Mashrura
  • Smith, Jennifer
  • Khan, Faisal
  • Veitch, Brian

Abstract

Offshore emergency conditions are dynamic in nature and personnel on board are challenged with high risk, time pressure, uncertainty, and the complexity of the situation. This paper investigates how different attributes of emergency scenarios influence people's choice of egress route subsequent to training. An empirical study was carried out in a virtual environment (VE) with 17 naïve participants. The participants were trained to muster during emergencies using a lecture based training (LBT) approach. Training sessions in LBT consisted of computer based training tutorials and simulated training scenarios. Participants’ performance was then tested in simulated testing scenarios. It was observed that given the same training, people used different sets of attributes to make decisions on the egress route. This can help to diagnose causes of poor performance and to design adaptive training lessons. Such identification can also help in the assessment of the efficacy of the training curriculum, or the pedagogical approach.

Suggested Citation

  • Musharraf, Mashrura & Smith, Jennifer & Khan, Faisal & Veitch, Brian, 2020. "Identifying route selection strategies in offshore emergency situations using decision trees," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:reensy:v:194:y:2020:i:c:s0951832017312516
    DOI: 10.1016/j.ress.2018.06.007
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    References listed on IDEAS

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    1. Musharraf, Mashrura & Smith, Jennifer & Khan, Faisal & Veitch, Brian & MacKinnon, Scott, 2016. "Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian Network approach," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 28-37.
    2. Shaw, Michael J & Gentry, James A & Piramuthu, Selwyn, 1990. "Introductive Learning Methods for Knowledge-Based Decision Support: A Comparative Analysis," Computer Science in Economics & Management, Kluwer;Society for Computational Economics, vol. 3(2), pages 147-165.
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    Cited by:

    1. Abaei, Mohammad Mahdi & Hekkenberg, Robert & BahooToroody, Ahmad, 2021. "A multinomial process tree for reliability assessment of machinery in autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    2. Seo, Seung-Kwon & Yoon, Young-Gak & Lee, Ju-sung & Na, Jonggeol & Lee, Chul-Jin, 2022. "Deep Neural Network-based Optimization Framework for Safety Evacuation Route during Toxic Gas Leak Incidents," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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