Fire probability prediction of offshore platform based on Dynamic Bayesian Network

In the manufacturing process, human errors will lead to defective products, even result in safety accidents. In this paper a manufacturing process risk analysis model considering human and organizational factors is built by integrating Human Factors Analysis and Classification System (HFACS) with Bayesian Network (BN). The human and organizational factors (HOF) causing process failure is qualitatively analyzed by HFACS. Then risk of the HOF is estimated by BN method, with the conditional probability of BN nodes evaluated by Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method. The proposed method is illustrated using an example study.

Human and organizational factors within the public sectors for the prevention and control of epidemic

Human and organizational factors (HOFs) play an important role in electric misoperation accidents (EMAs), but research into the reliability of human factors is still in its infancy in the field of EMAs, and further investment in research is urgently required. To analyze the HOFs in EMAs, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and fuzzy fault tree analysis (FFTA) was applied to EMAs for the first time in the paper. HFACS is used to identify and classify the HOFs with 135 accidents, reorganized as basic events (BEs), intermediate events (IEs), and top event (TE), and develop the architecture of fault tree (FT). Fuzzy aggregation is employed to address experts’ expressions and obtain the failure probabilities of the BEs and the minimal cut sets (MCSs) of the FT. The approach generates BEs failure probabilities without reliance on quantitative historical failure statistics of EMAs via qualitative records processing. The FFTA–HFACS model is applied for quantitative analysis of the probability of failure of electrical mishaps and the interaction between accident risk factors. It can assist professionals in deciding whether and where to take preventive or corrective actions and assist in knowledgeable decision-making around the electric operation and maintenance process. Finally, applying this hybrid method to EMAs, the results show that the probability of an EMAs is 1.0410 × 10−2, which is a risk level that is likely to occur and must be controlled. Two of the most important risk factors are habitual violations and supervisory violation; a combination of risk factors of inadequate work preparation and paralysis, and irresponsibility on the part of employees are also frequent errors.

Analysis of 2004 German general aviation aircraft accidents according to the HFACS model

Occurrence Human Factors Analysis Model is developed for China civil aviation based on the Human Factors Analysis and Classification System (HFACS) and the actual operation conditions and characteristics of China civil aviation to enhance the classification, analysis and utilization of safety information. In addition, to generate a model which can provides quantitative analysis supportive in aviation incident analysis, a human factor analysis model based on OHFAM and Bayesian network is established. This model constructed by combing hill-climbing search method with CH score function is a Bayesian network which uses three layers nodes to represent causality between human factors and incidents. The specific impact degree of human factors on aviation incidents is represented by conditional probability parameters of the model. It is useful in aviation incident analyses and deductions.

Understanding the interactions between contributory factors associated with accidents is important. However, the widely applied Swiss cheese model (SCM) and the human factors analysis and classification system (HFACS) oversimplify the causes of accidents and ignore the interactions among the various components of an entire system, which is the essence of systems thinking. A new systems‐based accident model that reflects the nature of the accidents and can be explained by the underlying accident model must therefore be developed. This study aims to create a new accident model that represents the interactions between the contributory factors, which can offer a systems approach to conducting a comprehensive examination of accidents. We use data obtained from the official accident reports on 94 extraordinary major coal mining catastrophes that took place from 1997 to 2011 in China. The results reveal that all latent conditions, including external factors, organizational influences, unsafe supervision, and preconditions for unsafe behaviours can influence active failures, that is, unsafe behaviours. However, only the preconditions for unsafe behaviours can influence unsafe behaviours within the SCM and HFACS frameworks. Furthermore, latent conditions can affect each other within the contributory factors interactions model. This study provides a new systems‐based accident model that allows us to examine and explain the relationships and linkages within and between complex sociotechnical systems and identify the common factors and interactions that cross human, external, and organizational factors.

With shrinking military budgets and the inevitable drawdown after the Cold War, the potential erosion of aircrew readiness/proficiency has been a source of concern within the U.S. Department of Defense. Unfortunately, it has been difficult to quantify proficiency using traditional performance measures. However, an analytic tool, the Human Factors Analysis and Classification System (HFACS) was recently developed that has enabled those within the Department of Defense and civilian aviation communities to examine pilot proficiency using accident data. An analysis of U.S. Navy/Marine Corps Class A aviation accidents occurring between fiscal years 1991 and 1998 was conducted using HFACS to determine what, if any, trends were evident that would indicate an erosion of pilot proficiency. Notably, a steady increase in the percentage of accidents associated with skill-based errors was observed beginning in 1991. To the degree that skill-based errors reflect a lack of proficiency, they provide an objective indicator of pilot ability. Several explanations have been suggested for this apparent trend including a reduction in flight hours, changes in aircrew training, and/or pilot retention. By employing HFACS, safety professionals can gain a better understanding of previously nebulous topics such as pilot proficiency.

Hot air balloons play a significant role in tourism in Türkiye. Despite offering a unique experience, they are also associated with a relatively high risk of accidents. According to the Turkish Ministry of Transport and Infrastructure (2024), hot air balloon accidents account for 36% of aviation incidents, highlighting the need for closer examination of these occurrences. Since human factors constitute the dominant contributory element in aviation accidents, it is crucial to analyze hot air balloon incidents from this perspective. This study examines two serious hot air balloon accidents in Türkiye that occurred between 2021 and 2024, focusing on the human factors involved. Using a mixed methodology, including the 5 Whys Root Cause Analysis (RCA) and the Human Factors Analysis and Classification System (HFACS), the research investigates systemic issues. Data were collected from official accident reports published by the Transport Safety Investigation Center under the Ministry of Transport and Infrastructure of the Republic of Türkiye. The analysis reveals significant patterns in decision-making errors, insufficient meteorological assessments, inadequate training, and organizational shortcomings. The findings show that both accidents were driven by systemic failures rather than pilot error alone. These included issues with organizational processes, supervisory oversight, and safety culture. The study emphasizes the need for improved meteorological systems, better training for adverse weather conditions, and enhanced safety management practices that stress the importance of a proactive safety culture and stronger operational planning to address human factors and reduce risks in hot air balloon operations.

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 standardized 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 organizations and the conduct of the studies.

Investigation into the role of human and organizational factors in security work against terrorism at large-scale events

Despite the modernization and automation in marine technology and the implementation of safety-related regulations, marine accidents are still a main concern for global maritime transportation. It is well known that human error is the key contributor, but human error data is hard to be collected and classified and the factors which influence human behavior should be identified. For this reason, the goal of this paper constructed a case-based HFACS (Human Factor Analysis and Classification System) method for marine human factor data collection and classification, and the data collected produced statics for analysis and safety management.

<h3>Introduction</h3> Outside of procedural adverse events, there is little understanding of wider patient safety incidents (PSIs) in endoscopy. The aim of this study was to quantify endoscopy PSIs and identify their contributory human factors utilising a national data set. <h3>Methods</h3> Data were extracted from the National Reporting and Learning System (NRLS) which records staff-reported safety incidents in England and Wales. Two independent coders with backgrounds in safety and qualitative analysis coded data using a hybrid thematic analysis approach. The Human Factors Analysis and Classification System (HFACS) was applied to code contributory factors. <h3>Results</h3> From 2017-2019, 1811 endoscopy-related PSIs were identified, of which 629 were procedural adverse events (pAEs; directly related to procedure), 539 were non-procedural adverse events (nAEs; any incident not directly related to a procedure) and 16 were ‘never’ events. Inter-coder reliability was substantial (kappa 0.77). 842 human factors codes were identified across four levels: acts, preconditions, supervision and organisational influences. Decision-based errors were the most common acts (&gt;40%) across categories (table 1). Patient factors were significant contributors in pAEs (74.5%) and co-ordination, communication (33.5 – 66.7%) and situational (27.1%) factors were key contributors in nAEs and never events. <h3>Conclusions</h3> This is the first overview of national-level endoscopy safety incident data and demonstrates the role human factors play in PSI development. These findings should inform patient safety improvement strategies in endoscopy.

An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017

Electric power industry is an important basic industry of national economy and an important public utility. Electric power safety is directly related to the development of national economy and the safety of people’s life and property. In order to improve the capacity of producing electric energy safely and reduce the number of electric power personal accidents, this paper focuses on the human factor of electric power accident. Firstly, a new framework suitable for power electric personal accident analysis is constructed by using the human factors analysis and classification system (HFACS). Secondly, 173 cases of accidents from 2015 to 2018 are analysed. Thirdly, the latent class analysis (LCA) method is used to cluster these cases to find out the hidden category characteristics and analyse the correlation between the causes of particular category accidents. Finally, the corresponding management countermeasures and suggestions are put forward according to different situations. The results show that, in addition to the general characteristics of power personal accidents, five major accident categories are identified by LCA method, which have their own prominent characteristics and different causative processes in terms of human factors.KeywordsElectric power personal accidentHFACSLCAAccident causesAccident characteristics

With the global warming and a large amount of sea ice melting, the available Arctic Sea Route has greatly enhanced the value of Arctic shipping. Ship operations under icebreaker assistance have become an essential way to facilitate the safe navigation of merchant vessels sailing through the Arctic Sea Route in ice-covered waters, but they can also put the crew and the ship in danger caused by a possible collision between the assisted ship and the icebreaker. In this paper, a dedicated Human and Organizational Factors (HoFs) model of ship collision accidents between an assisted ship and an icebreaker is developed and analyzed with the aim to identify and classify collision risk factors. First, a modified model of the Human Factors Analysis and Classification System (HFACS) for collision accidents between a ship and an icebreaker in ice-covered waters is proposed, which helps to analyze ship collision reports. Then, a Fault Tree Analysis (FTA) model is utilized to analyze the fundamental collision risk factors according to the statistical analysis of accident reports and expert judgments based on the HFACS-SIBCI model. Finally, qualitative analysis is carried out to analyze collision risk factors under icebreaker assistance, where Risk Control Options (RCOs) are formulated. An important guidance for the risk control of ship collisions during icebreaker assistance in ice-covered waters is provided for lawmakers and shipping companies.