Human Factors Analysis And Classification System Model Research Articles

Dynamic risk analysis for oil and gas exploration operations based on HFACS model and Bayesian network

Oil and gas exploration (OGE) is an important part of the petroleum industry. How to predict and prevent risks of OGE accidents in a targeted way is still a challenge, while few researchers have focused on this issue because of the lack of data from the worksite. Therefore, the accident cases for decades of years in a globally operated OGE company are collected as a basis, a dynamic risk analysis method combining the human factors analysis and classification system (HFACS) and the Bayesian network (BN) has been proposed in this article. First, identify the accident cases, analysis, and divided the risk factors into four levels and 27 classification categories, and setup a HFACS-OGE model. Then, map the factors to the BN and identify the correlation of the secondary level factors. Finally, setup a BN model with the Genie software. Through posterior probability analysis and sensitivity analysis, we conclude the major and sensitive factors leading to OGE accidents. According to the conclusion, administrators could decide on corresponding management and control methods. This method can hopefully provide an effective quantitative basis for the identification and prediction of the factors that may lead to OGE accidents.

A methodology to assess the causation relationship of seafarers’ unsafe acts for ship grounding accidents based on Bayesian SEM

Unsafe acts have been recognized as the substantial factors leading to maritime accidents. An obstacle to analyzing seafarers' unsafe acts involved in maritime accidents is the lack of data. Bayesian approach in structural equation modelling (SEM) estimation has attracted extensive attentions across other fields, due to its advantages that can handle complicated models and small sample size. Therefore, this study proposes an innovative methodology to address the multifactor risk coupling and causation involved in seafarers' unsafe act contributing to ship grounding accidents. Based on the principle of Bayesian SEM, the advantages of Grounded theory and Human Factors Analysis and Classification System (HFACS) are integrated into this methodology. Specifically, Grounded theory and HFACS model are applied to identify and structure seafarers' unsafe acts and their contributory factors, facilitating the development of the conceptual model. Then, by employing Bayesian SEM, the causation paths and effects of seafarers' unsafe acts are examined. The results reveal that organizational influences play a significant role in the causation mechanism of seafarers' unsafe acts, and preconditions for unsafe acts are the only variables that exert direct impact on unsafe acts. Moreover, unsafe supervision and preconditions for unsafe acts are served as intermediate factors affecting seafarers' unsafe acts. In addition, this results indicate the feasibility of Bayesian SEM in analyzing seafarers’ unsafe acts.

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

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.

Application of HFACS and grounded theory for identifying risk factors of air traffic controllers’ unsafe acts

Unsafe acts of air traffic controllers (ATCers) are caused by various factors. Based on interview data and case reports, human factors analysis and classification system (HFACS) and the grounded theory were adopted to identify the risk factors of ATCers’ unsafe acts comprehensively. The interview data and the case data issued by the authority were first collected. Then, the above data were encoded to obtain the relevant concepts and categories based on the grounded theory, and the HFACS model is used to classify the concepts and categories. Finally, the relationship between the core category and the secondary category was sorted out in the way of storyline. The results show that the risk factors include environmental factors, organizational influences, unsafe supervision and controllers’ states, and the unsafe acts manifest as errors and violations. Among them, the controllers’ states are intermediate variable, and other factors indirectly affect the controllers’ unsafe acts. The first three risk factors with high frequency in unsafe incidents are technical environment, mental states and business ability. The three most common unsafe acts are giving the wrong order, insufficient situational awareness, and poor work order on-site. Through combining HFACS framework and grounded theory to analyze data, a more clear and comprehensive conceptual model of risk factors of ATCers’ unsafe acts can be obtained.

Evolution Mechanism on the Unsafe Behavioural Risks of General Aviation Pilots

The unsafe behaviour prevention and control of general aviation pilots has become an emphasis in the general aviation safety management with the increasing number of general aviation enterprises, lengthening of flight time and frequent occurrence of public safety events caused by general aviation accidents. How to identify the factors influencing the unsafe behaviours of general aviation pilots and clarify the inter factor evolution mechanism is hot issue in the general aviation. To accurately identify the key factors influencing the unsafe behaviours of general aviation pilots and define the interaction mechanism between factors, using the unsafe behaviours of pilots in 200 global general aviation accidents during 2015-2019 and the association rule method, the bottom-layer factors of the Human Factors Analysis and Classification System (HFACS) model were analysed. Furthermore, the influence degree, influenced degree, centrality and causality of the influencing factors in the HFACS model were calculated, and the risk transfer path at different layers was determined on the basis of the integrated decision-making trial and evaluation laboratory (DEMATEL) and fuzzy theory. Results show that the poor individual ready state is strongly associated with skill error, decision-making error and habitual violation. Moreover, 11 factors, such as poor physical environment, physical/intelligence limitation and poor technical environment, constitute the factors in the cause group for pilot unsafe behaviours. 7 factors, such as insufficient supervision, improper operation plan and failure to discover and correct problems, are the factors in the result group. Illegal behaviour, failure to discover and correct problems and decision-making error of pilots, which are of high centrality, are key factors influencing the unsafe behaviours of general aviation pilots. The conclusions obtained from this study compensate the deficiencies for the linear statistical model of risk factors and provide a novel method for regulating and controlling the unsafe behaviours of general aviation pilots.

Human Factors Analysis and Classification System (HFACS): As Applied to Asiana Airlines Flight 214

The Human Factors Analysis and Classification System (HFACS) is a safety tool that aids in the identification and analysis of organizational factors that contribute to aircraft accidents. By using the HFACS model, safety investigators can better understand the existing conditions that contribute to accidents, which then allows for the development and implementation of safety programs to prevent these conditions. In this study, the HFACS framework was utilized to identify the human factors that contributed to the Asiana Airlines flight 214 accident that occurred on July 6, 2013. The results of this study indicate that inadequate pilot training, lack of upper-level supervision, and recurring deviation from standard operating procedures (SOPs) largely contributed to this accident. These findings emphasize the various organizational levels that serve a role in aviation accidents, highlighting the importance of practicing a proactive approach to safety and mitigating hazards within the upper levels of an organization before they lead to disaster on the front line.

Analysis of "2·28" KEEPER Chemical Industries Hazardous Chemical Explosion Accident Based on FTA and HFACS.

On 28 February 2012, a guanidine nitrate explosion occurred at HEBEI KEEPER Chemical Industries Co., Ltd., China, resulting in 25 deaths, with 4 missing individuals and 46 injured. In order to explore the causal relationship hidden behind this accident, fault tree analysis (FTA) and the Human Factors Analysis and Classification System (HFACS) were used to systematically analyze the incident. Firstly, FTA was used to analyze the causes of the accident in depth, until all the basic causal events causing the guanidine nitrate explosion were identified, and a fault tree diagram of the guanidine nitrate explosion was drawn. Secondly, for the unsafe acts in the basic causal events, the HFACS model was used to analyze the three levels of factors that lead to unsafe acts, including the preconditions for unsafe acts, unsafe supervision, and organizational influences. Finally, based on the analysis results of FTA and HFACS, a complete logic diagram of the causes of the accident was obtained. The FTA and HFACS accident analysis methods allowed for the identification of human factors and the accident evolution process in the explosion accident and provide a reference for accident investigation.

A marine accident analysing model to evaluate potential operational causes in cargo ships

This paper introduces a novel hybrid approach to assess potential operational contingencies in a real shipboard accident since safety at sea is of paramount significance in maritime transportation industry. The hybrid accident analysis model integrates an Analytical Network Process (ANP) method with Human Factors Analysis and Classification System (HFACS). To achieve this purpose, the HFACS model provides a schematic conceptual framework to investigate and analyse role of human error in marine accident and the ANP method provides correlation among the factors for assessment. Thus, the most important factors that contribute to the accident are revealed respectively. The novelty of this paper is to present a different perspective during marine accident analysis in which priority weights of accident causes related to the human error are calculated by ANP model. The hybrid accident analysis model is established to enhance safety and prevent loss of life or injury in maritime transportation industry. The proposed hybrid approach is illustrated with a real-ship incident case: a serious liquefied petroleum gas leak from the gas carrier ship. In conclusion, the research is expected to encourage safety researchers and ship-management companies to prevent similar accident occurrence.

Coding ATC incident data using HFACS: Inter-coder consensus

Reliability studies for coding contributing factors of incident reports in high hazard industries are rarely conducted and reported. Although the Human Factors Analysis and Classification System (HFACS) appears to have a larger number of such studies completed than most other systems doubt exists as the accuracy and comparability of results between studies due to aspects of methodology and reporting. This paper reports on a trial conducted on HFACS to determine its reliability in the context of military air traffic control (ATC). Two groups participated in the trial: one group comprised of specialists in the field of human factors and the other group comprised air traffic controllers. All participants were given standardised training via a self-paced workbook and then read 14 incident reports and coded the associated findings. The results show similarly low consensus for both groups of participants. Several reasons for the results are proposed associated with the HFACS model, the context within which incident reporting occurs in real organisations and the conduct of the studies.