Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian Network approach

Musharraf, Mashrura; Smith, Jennifer; Khan, Faisal; Veitch, Brian; MacKinnon, Scott

doi:10.1016/j.ress.2016.02.001

Cited by 62 publications

(22 citation statements)

References 13 publications

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“…Bayesian network (BN) is a well-known graphical inference method which expresses the causal relationships between the causes and final outcomes in a system (Rausand, 2011). In addition, BN has also been widely used in various engineering applications such as risk and reliability assessment (Abimbola et al, 2015;Bouejla et al, 2014;Hänninen et al, 2014;Huang et al, 2006;Khakzad et al, 2013a, Yuan et al, 2015, improving the safety performance of a system ( Herrero et al, 2013;John et al, 2016;Trucco et al, 2008), updating failure probability (Leu and Chang, 2013;Musharraf et al, 2016;Wu et al, 2015), and mapping static or dynamic FTs into corresponding BNs (Barua et al, 2016;Hänninen et al, 2014;Kabir et al, 2014;Khakzad et al, 2013aKhakzad et al, , 2013bMarvin et al, 2017;Pereira et al, 2016;Yeo et al, 2016;Zarei et al, 2017). In a BN model, both forward and backward analysis could be performed.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy Bayesian network approach for risk analysis in process industries

Yazdi

Kabir

2017

Process Safety and Environmental Protection

225

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Fault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian Network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant.

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Section: Introductionmentioning

confidence: 99%

A fuzzy Bayesian network approach for risk analysis in process industries

Yazdi

Kabir

2017

Process Safety and Environmental Protection

225

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“…The data presented in this article were originally collected during an experimental study presented in [2] , [3] . Though a broad range of human performance data were collected during the study, this article only presents the data relevant to the article “Incorporating individual differences in human reliability analysis: an extension to the virtual experimental technique” [1] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Human performance data collected in a virtual environment

et al. 2017

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This data article describes the experimental data used in the research article “Incorporating individual differences in human reliability analysis: an extension to the virtual experimental technique” (Musharraf et al., 2017) [1]. The article provides human performance data for 36 individuals collected using a virtual environment. Each participant was assigned to one of two groups for training: 1) G1: high level training and 2) G2: low level training. Participants’ performance was tested in 4 different virtual scenarios with different levels of visibility and complexity. Several performance metrics of the participants were recorded during each scenario. The metrics include: time to muster, time spent running, interaction with fire doors and watertight doors, interaction with hazards, and reporting at different muster locations.

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“…Trainer. It is a desktop-based virtual environment that replicates an offshore petroleum facility in which users can learn knowledge and skills regarding offshore emergency safety procedures [12].…”

Section: All Hands Virtual Emergency Response Trainer Simulator Avermentioning

confidence: 99%

Using Simulator Data to Facilitate Human Reliability Analysis

Musharraf

Moyle

Khan

et al. 2019

Journal of Offshore Mechanics and Arctic Engineering

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Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in offshore emergency situations using data generated in a simulator. Assessment begins by using constrained noninformative priors to define the HEPs in emergency situations. An experiment is then conducted in a simulator to collect human performance data in a set of emergency scenarios. Data collected during the experiment are used to update the priors and obtain informed posteriors. Use of the informed posteriors enables better understanding of the performance, and a more reliable and objective assessment of human reliability, compared to traditional assessment using expert judgment.

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Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian Network approach

Cited by 62 publications

References 13 publications

A fuzzy Bayesian network approach for risk analysis in process industries

A fuzzy Bayesian network approach for risk analysis in process industries

Human performance data collected in a virtual environment

Using Simulator Data to Facilitate Human Reliability Analysis

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