On the use of the hybrid causal logic method in offshore risk analysis

Røed, Willy; Mosleh, Ali; Vinnem, Jan Erik; Aven, Terje

doi:10.1016/j.ress.2008.04.003

Cited by 108 publications

(63 citation statements)

References 6 publications

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“…Causes for omission are not further included in the model, so that the probability of omission is based on historical data. The HCL model is described in an application paper in offshore risk analysis (Røed et al, 2009). The RIFs are selected from the BORA project, but the RIFs can be linked either to another RIF, or to a basic event in the fault tree.…”

Section: Rifs From Different Modelsmentioning

confidence: 99%

“…Identify how risk factors can be linked to derive corresponding risk level Eight models have been identified with keyword "risk influence" and "major accident" from published articles. The models are MACHINE (Model of Accident Causation using Hierarchical Influence Network) (Embrey, 1992), SAM (System Action Management) (Paté-Cornell and Murphy, 1996), ω-factor model (Mosleh et al, 1997;Mosleh and Goldfeiz, 1999), I-RISK (Integrated Risk) (Papazoglou et al, 2002), ORIM (Organizational Risk Influence Model) model (Øien, 2001a, b), BORA (Barrier and Organizational Risk Analysis) (Aven et al, 2006), HCL (Hybrid Causal Logic), which has been applied to oil and gas industry by Røed et al (2009), RISK_OMT (Vinnem et al, 2012), SoTeRiA (Social-Technical Risk Analysis ) ) and Phoenix (Ekanem et al, 2016). In addition, WPAM (Work Process Analysis Model) (Davoudian et al, 1994) has also been included due to its activity-oriented nature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk influence frameworks for activity-related risk analysis during operation: A literature review

Yang

Haugen

2017

Safety Science

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Experience gained in the petroleum activities have showed that major accident risk is inherent in daily activities. Risk influence methodology is perceived as a good candidate to model the activity-related risk, as a key input to operational planning decisions. The paper reviews and summarizes 11 risk influence frameworks that integrate organizational and human factors in a structured way. The intention was to evaluate how these frameworks and identified risk influencing factors (RIFs) can be used for activity-related risk analysis. The main conclusion is that it is not necessary to model explicitly RIFs for activity consequence risk -the effect that performing an activity will have on the risk level after the activity has been completed. Operational management RIFs, direct organizational RIFs, personal risk influencing factors, task characteristics RIFs, technical system RIFs, and environmental RIFs are relevant for activity performance risk -the risk associated with performing the action. Operational management RIFs influence planning, which is important to identification of interactions while estimating period risk -the risk for a plant or facility over a period.

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Section: Rifs From Different Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk influence frameworks for activity-related risk analysis during operation: A literature review

Yang

Haugen

2017

Safety Science

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“…Røed et al [5] propose an interesting approach for the analysis of risk on petroleum installations. Their approach allows some parts of the risk assessment to be addressed using fault trees, while other parts are addressed using Bayesian belief networks.…”

Section: Related Workmentioning

confidence: 99%

A Model-Based Approach to Support Safety-Related Decisions in the Petroleum Domain

Montecchi

Refsdal

Lollini

et al. 2016

2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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Abstract-Accidents on petroleum installations can have huge consequences; to mitigate the risk, a number of safety barriers are devised. Faults and unexpected events may cause barriers to temporarily deviate from their nominal state. For safety reasons, a work permit process is in place: decision makers accept or reject work permits based on the current state of barriers. However, this is difficult to estimate, as it depends on a multitude of physical, technical and human factors. Information obtained from different sources needs to be aggregated by humans, typically within a limited amount of time. In this paper we propose an approach to provide an automated decision support to the work permit system, which consists in the evaluation of quantitative measures of the risk associated with the execution of work. The approach relies on state-based stochastic models, which can be automatically composed based on the work permit to be examined.

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“…Many of the introduced methods allow greater levels of detail and accuracy in modeling phenomena and processes covering physical phenomena. Human and organizational factors, as well as the use of software, made it a more dynamic analysis (Luxhøj and Coit, 2006;Mohaghegh et al, 2008;Ale et al, 2009;Røed et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Method for measuring factors that affect the performance of pilots

Bandeira¹,

Correia²,

Martins

2017

TRANSPORTES

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This paper presents the development of a model of accident analysis according to the principal factors which influence aeronautical accidents that are able to assess any aircraft accident, taking into account human, organizational, environmental and airport infrastructure factors. The methodology of data collection of this research was through the literature, analysis of aircraft accident reports, technical visits to the center of certification of commercial aircraft pilots and interviews with industry experts. From this model, it is possible to evaluate the influence of these factors and identify the dependence and relationship existing, and how they influence the system. With the aid of Bayesian Networks technique, it is also possible to quantify the factors and assess which ones have more impact in the system. The results show the relationship between the factors that can influence the performance of the pilots and therefore can indicate how it may impact the success or failure of tasks related to flight procedures. The results also may indicate subsidies for mitigating actions, collaborating in the management of operational safety of air transport and assessing the overall impact of the factors that determine any accident. RESUMOEste artigo apresenta o desenvolvimento de um modelo de análise de acidentes de acordo com os principais fatores que influenciam os acidentes aeronáuticos, capaz de avaliar qualquer acidente de aeronave, levando em conta fatores de infraestrutura humana, organizacional, ambiental e aeroportuária. A metodologia de coleta de dados desta pesquisa foi através de literatura, análise de acidentes aeronáuticos, visita técnica no centro de certificação de pilotos de aeronaves comerciais e consultas a especialistas. A partir desse modelo, é possível avaliar a influência desses fatores e identificar as dependências e relações existentes, e como elas influenciam o sistema. Com a ajuda da técnica de Redes Bayesianas também é possível quantificar os fatores e avaliar quais fatores têm mais impacto no sistema. Os resultados mostram a relação entre os fatores que podem influenciar o desempenho dos pilotos e, portanto, indicam como isso pode afetar o sucesso ou o fracasso das tarefas relacionadas aos procedimentos de voo. Os resultados também podem indicar subsídios para ações mitigadoras, colaborar na gestão da segurança operacional do transporte aéreo e avaliar o impacto global dos fatores que determinam qualquer acidente.

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On the use of the hybrid causal logic method in offshore risk analysis

Cited by 108 publications

References 6 publications

Risk influence frameworks for activity-related risk analysis during operation: A literature review

Risk influence frameworks for activity-related risk analysis during operation: A literature review

A Model-Based Approach to Support Safety-Related Decisions in the Petroleum Domain

Method for measuring factors that affect the performance of pilots

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