HFACS-based FAHP implementation to identify critical factors influencing human error occurrence in nuclear plant control room

Karthick, M.; Robert, Tanguy; Kumar, Chandan

doi:10.1007/s00500-020-04961-1

Cited by 18 publications

(9 citation statements)

References 40 publications

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“…They identified attention, perception, memory, decision-making, training, and communication as critical factors contributing to human errors. Their results also showed that the HFACS-based FAHP methodology can determine the intrinsic human factors contributing to the performance of NPP's CR operators [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

Looking beyond the screen: A systematic review of safety in control rooms

Janizadeh,

Choobineh,

Charkhabi

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

Looking beyond the screen: A systematic review of safety in control rooms

Janizadeh,

Choobineh,

Charkhabi

et al. 2024

Heliyon

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“…In the chemical industry, based on the HFACS framework, Wang et al ( 19 ) obtained a new model, HFACS-CSMEs (Human Factor Analysis and Classification System for Chemical Small and Medium-sized Enterprises), which can effectively identify and distinguish the causes of chemical accidents, providing a new idea for accident prevention of small and medium-sized chemical enterprises. In the power industry, based on the HFACS framework and fuzzy analytic hierarchy process, Karthick et al ( 20 ) analyzed the internal human factors affecting the operators' performance in nuclear power plants. They pointed out that the key factors leading to human error were cognitive and organizational.…”

Section: Methodsmentioning

confidence: 99%

Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS

Yang

Wang

Zhu

et al. 2022

Front. Public Health

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The risk factors affecting workers' unsafe acts were comprehensively identified by Human Factors Analysis and Classification System (HFACS) and grounded theory based on interview data and accident reports from deep coal mines. Firstly, we collected accident case and field interview data from deep coal mines issued by authoritative institutions. Then, the data were coded according to grounded theory to obtain relevant concepts and types. The HFACS model was used to classify the concepts and categories. Finally, the relationship between core and secondary categories was sorted out by applying a story plot. The results show that risk factors of unsafe acts of deep coal mine workers include environmental factors, organizational influence, unsafe supervision and unsafe state of miners, and the main manifestations of unsafe acts are errors and violations. Among them, the unsafe state of miners is the intermediate variable, and other factors indirectly affect risky actions of coal miners through unsafe sates. Resource management, organizational processes and failure to correct problems are the top three risk factors that occur more frequently in unsafe acts. The three most common types of unsafe act are unreasonable labor organization, failure to enforce rules, and inadequate technical specifications. By combining grounded theory and the HFACS framework to analyze data, risk factors for deep coal miners can be quickly identified, and more precise and comprehensive conceptual models of risk factors in unsafe acts of deep coal miners can be obtained.

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“…To improve the shortcomings of the traditional HFACS model for accident analysis, it is combined other methods. For example, the Fuzzy AHP has been combined with the HFACS framework to identify the key factors of human error in the control room of a nuclear power plant [21]. The AHP and fuzzy TOPSIS were combined with the HFACS method, to identify and prioritize failure modes based on the analysis of 157 adverse medical events [17].…”

Section: Hfacsmentioning

confidence: 99%

A hybrid model integrating FMEA and HFACS to assess the risk of inter-city bus accidents

Liou

Liu

Luo

et al. 2022

Complex Intell. Syst.

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The incidence of inter-city bus accidents receives a lot of attention from the public because they often cause heavy casualties. The Human Factors Analysis and Classification System (HFACS) is the prevailing tool used for traffic accident risk assessment. However, it has several shortcomings, for example: (1) it can only identify the potential failure modes, but lacks the capability for quantitative risk assessment; (2) it neglects the severity, occurrence and detection of different failure modes; (3) it is unable to identify the degree of risk and priorities of the failure modes. This study proposes a novel hybrid model to overcome these problems. First, the HFACS is applied to enumerate the failure modes of inter-city bus operation. Second, the Z-number-based best–worst method is used to determine the weights of the risk factors based on the failure mode and effects analysis results. Then, a Z-number-based weighted aggregated sum product Assessment is utilized to calculate the degree of risk of the failure modes and the priorities for improvement. The results of this study determine the top three ranking failure modes, which are personal readiness from pre-conditions for unsafe behavior, human resources from organizational influence, and driver decision-making error from unsafe behavior. Finally, data for inter-city buses in Taiwan in a case study to illustrate the usefulness and effectiveness of the proposed model. In addition, some management implications are provided.

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HFACS-based FAHP implementation to identify critical factors influencing human error occurrence in nuclear plant control room

Cited by 18 publications

References 40 publications

Looking beyond the screen: A systematic review of safety in control rooms

Looking beyond the screen: A systematic review of safety in control rooms

Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS

A hybrid model integrating FMEA and HFACS to assess the risk of inter-city bus accidents

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