Human-centered modeling in human reliability analysis: some trends based on case studies

Mosneron-Dupin, F.; Reer, B.; Heslinga, G.; Sträter, Oliver; Gerdes, V.; Saliou, G.; Ullwer, W.

doi:10.1016/s0951-8320(97)00081-1

Cited by 32 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analysis via behavioral patterns builds on literature works on models of crew response in emergency situations for simulation-based applications 27 and retrospective analysis of past event. 28 Similarly to the present paper, both works interpret variability in crew behaviors as the result of the dynamic interaction between crew-specific and task-, context-related factors (modelled by ''performance adjustment factors'' in Woods et al 27 and by ''situation factors'' in Mosneron-Dupin et al 28 ). However, neither of these works had the objective of incorporating performance variability in HEP quantification.…”

Section: Introductionmentioning

confidence: 75%

“…In the long term, as more data on crew behaviors may be available, consolidated sets of behavioral categories may be identified and reused across studies to investigate their relative importance and impact on crew performance. As mentioned, this ''library of categories'' would identify the categories relevant for groups of F, ideally defined to group situations by type, for example ''fast-pacing,'' ''standard procedure-following,'' ''conflicting goals'' in a similar way as proposed in Mosneron-Dupin et al 28 Also, with more data available, data analysis and statistical tests could be used to derive the groups (e.g. via cluster analysis), identify dominant categories, and rule out or aggregate categories with limited impact on task performance and support accordingly the library of categories, reducing the subjective component in category definitions.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

Greco

Podofillini

Dang

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

Current Human Reliability Analysis models express error probabilities as a function of task types and operational context, without explicitly modelling the influence of different crew behavioral characteristics on the error probability. The influence of such variability is treated only implicitly, by variability and uncertainty distributions with bounds primarily obtained by expert judgment. This paper presents a methodology to empirically incorporate crew performance variability in error probability quantification, from simulator data. Crew behaviors are represented by a set of “behavioral patterns” that emerge in the observation of operating crews (e.g. in information sharing or in adhering to procedural guidance). The paper demonstrates the use of a Bayesian hierarchical model to explicitly capture the performance variability emerging from data. The methodology is applied to a case study from literature. Numerical demonstrations are performed in order to compare the proposed approach to the existing quantification models used in HRA for treating simulator data.

show abstract

Section: Introductionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

Greco

Podofillini

Dang

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…It is particularly doubtful whether meaningful numerical probabilities can be assigned to human reactions to new and untested technologies. These critics argue that attention should be redirected towards qualitative or human-centered approaches that aim at identifying new types of human failure rather than at quantifying currently known failure types (Hollnagel 1991;Mosneron-Dupin et al 1997).…”

Section: Integrated System Approachmentioning

confidence: 99%

Should Probabilistic Design Replace Safety Factors?

Doorn

Hansson

2010

Philos. Technol.

View full text Add to dashboard Cite

Safety is a concern in almost all branches of engineering. Whereas safety was traditionally introduced by applying safety factors or margins to the calculated maximum load, this approach is increasingly replaced with probabilistic risk assessment (PRA) as a tool for dimensioning safety measures. In this paper, the two approaches are compared in terms of what they aim at and what they can, in fact, achieve. The outcome of this comparison suggests that the two approaches should be seen as complementary rather than mutually exclusive. PRA is particularly useful for priority setting and for the effect evaluation of safety measures; however, in most applications, uncertainties prevent PRA from providing an objective probability of failure or value of damage. Safety factors are indispensible for dealing with dangers that cannot be assigned meaningful probabilities.

show abstract

“…• tendency to adhere to practical routines that legitimate the decision-maker, in situations of increased stress [24]; • "involuntary rest" 14 , i.e., the repetition of the same sequence of stimuli-reply leads to a neuric state of quasifatigue that requires rest, during which no attention is paid to the task. Cognitive models (Skill-Rule-Knowledge, SRK 15 ; Reason's Absentmindedness model [25]; Systematic Human Action Reliability Procedure, SHARP 16 ; and Task Analysis-Linked Evaluation Technique, TALENT [26]) distinguish different levels of decision-making and examine the behaviour (and sorts of error) in each of these levels.…”

Section: Human Error Modelsmentioning

confidence: 99%