An accident causation model based on safety information cognition and its application

Yuan-jiang, Chen; Wu, Feng; Jiang, Zhiqiang; Duan, Lingling; Cheng, Shuiyuan

doi:10.1016/j.ress.2020.107363

Cited by 27 publications

(36 citation statements)

References 22 publications

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“…Chen et al (2017) applied the case study to illustrate a safety barrier-based accident model for addressing accident causations. Chen et al (2021) developed a safety information cognition-based accident-causing model and used the electrical fire as the case to demonstrate its viability. In the construction industry, Tong et al (2020) constructed a modified accident causation model for highway construction accidents and the model was verified with a tunnel collapse accident.…”

Section: Accident Causation Investigation On Case Studiesmentioning

confidence: 99%

Knowledge discovery of correlations between unsafe behaviors within construction accidents

Guo

Zhao

Luoren

et al. 2021

ECAM

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PurposeKnowledge discovery related to unsafe behaviors promotes the performance of accident prevention in construction. Although numerous studies on accident causation models have discussed the correlations of unsafe behaviors with various factors (e.g., unsafe conditions), limited research explores correlations between unsafe behaviors within accidents. The purpose of this paper is mining strong association rules of unsafe behaviors from historical accidents to clarify this kind of tacit knowledge.Design/methodology/approachA case study was adopted as the research approach, in which accident records from building and urban railway construction in China were selected as data resources. The groups of unsafe behaviors extracted from accident records were expressed by the definitions of unsafe behaviors from safety regulations and operating procedures. Frequent Pattern (FP)-Growth algorithm was used for association rule mining, and the critical correlations between unsafe behaviors were represented by the effective strong rules.FindingsThe findings identify and distinguish correlations between unsafe behaviors within construction accidents. In building construction, workers and managers should pay attention to preventing unsafe behaviors related to personal protective equipment and machines and equipment. In urban railway construction, workers should especially avoid unsafe behaviors of inadequately dealing with environmental factors.Practical implicationsTacit knowledge is transferred to explicit knowledge as the critical correlations between unsafe behaviors within accidents are determined by the effective strong rules. Additionally, the findings provide practice guidance for safety management, to collaboratively control unsafe behaviors with strong correlations.Originality/valueThis study contributes to the body of safety knowledge in construction and provides a further understanding of how construction accidents are caused by multiple unsafe behaviors.

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Section: Accident Causation Investigation On Case Studiesmentioning

confidence: 99%

Knowledge discovery of correlations between unsafe behaviors within construction accidents

Guo

Zhao

Luoren

et al. 2021

ECAM

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“…Human error is a critical factor causing widespread adverse consequences in various industrial facilities. 1,2 As a part of probabilistic safety assessment, human reliability analysis (HRA) is an active method used to identify, quantify and reduce human errors in human-machine systems. 3,4 Its purpose is to analyze vulnerabilities in tasks and operations, understand error cycle and forming factors, quantify potential errors, and finally, provide guidance for improving the system's reliability.…”

Section: Introductionmentioning

confidence: 99%

“…Human error is a critical factor causing widespread adverse consequences in various industrial facilities 1,2 . As a part of probabilistic safety assessment, human reliability analysis (HRA) is an active method used to identify, quantify and reduce human errors in human‐machine systems 3,4 .…”

Section: Introductionmentioning

confidence: 99%

An HFM‐CREAM model for the assessment of human reliability and quantification

Lin

Huang

2022

Quality & Reliability Eng

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Human reliability analysis (HRA) is a proactive approach to model and evaluate systematic human errors and has been extensively implemented in various complicated systems. The assessment of human errors relies heavily on the knowledge and experience of experts in real‐world cases. Moreover, there are usually specific sorts of uncertainty while experts use linguistic labels to evaluate human failure events. In this context, this paper seeks to establish a new model based on the hesitant fuzzy matrix (HFM) and the cognitive reliability and error analysis method (CREAM) to conduct a quantitative analysis of human errors. This model handles the multiple crisp scores of the common performance conditions (CPCs) given by experts according to the context description in terms of CPCs, determines the weights of CPCs by the HFM, and elicits the human error probability (HEP) point estimation formula considering consequences based on the CREAM. Finally, the effectiveness and practicality of the presented HFM‐CREAM model are demonstrated through the emergency response analysis of the steam generator tube rupture (SGTR) in nuclear power plant.

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“…As a result, the development of society is inseparable from the operation of a system; the operation of the system needs people’s participation and promotion. However, studies have shown that occupational accidents and large-scale industrial accidents are often attributed to human errors [ 2 ], accounting for more than 80% of accidents [ 3 ]. According to statistics, nearly 70–80% of major accidents in high-risk industries can be attributed to human error [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, further improved or more applicable human error analysis models were generated, such as HFACS-MA [ 18 ], HFACS-OGI [ 19 ], HFACS-CM [ 16 ], etc. In addition, there is another HFA model from the perspective of information and cognition [ 2 , 20 , 21 ]. In recent years, with the development of safety information cognition, this perspective also developed from the micro-level to the macro-level.…”

Section: Introductionmentioning

confidence: 99%

Human Factor Analysis (HFA) Based on a Complex Network and Its Application in Gas Explosion Accidents

Zhang

2022

IJERPH

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Humans are at the core of the social-technical system, and their behavioral errors affect the reliability and safety of the entire system in varying degrees. Occupational accidents and large-scale industrial accidents are often attributed to human errors, accounting for more than 80% of accidents. In view of the complexity of systems and the coupling of elements, a new HFA method is proposed based on a complex network. According to system safety theory, a complex network is regarded as a network composed of humans, matters, environments, and management, and the basic structure of the HFA network is summarized. On this basis, a system safety method of HFA is developed which proposes a universal human error causation model. Moreover, a network analysis method for human errors is also presented, which is a comprehensive analysis of human errors that have occurred. Finally, the above methods are applied to gas explosion accidents that occurred in China. Results show that the two methods proposed are universal to all fields, and their combination improves the effectiveness of human error management and promotes the targeted, proactive, systematic, and dynamic prevention of critical nodes and paths from a holistic perspective.

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An accident causation model based on safety information cognition and its application

Cited by 27 publications

References 22 publications

Knowledge discovery of correlations between unsafe behaviors within construction accidents

Knowledge discovery of correlations between unsafe behaviors within construction accidents

An HFM‐CREAM model for the assessment of human reliability and quantification

Human Factor Analysis (HFA) Based on a Complex Network and Its Application in Gas Explosion Accidents

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