Engineering ethics within accident analysis models

An Accident Causation Analysis and Taxonomy (ACAT) model of complex industrial system from both system safety and control theory perspectives

Offshore oil and gas production and processing facilities are prone to incidents such as leakage which may escalate thus causing major accidents. These accidents pose a serious threat to personnel and assets. Previously, accident modelling has relied on studying a single event and its impact. It has been witnessed from past events that accidents are caused by combinations of events and therefore, accident modelling must consider multiple sequences of events and interdependent factors. The Floating Liquefied Natural Gas (FLNG) is a complex processing facility where a leakage of liquified natural gas (LNG) may escalate to a range of events such as fire to vapor cloud explosion. The escalation of events is dependent on the multiple intertwined factors evolving with time and space. This study is focussed on developing novel methodologies and models to study the transitional events and their causation during a major event in complex LNG processing facilities. This thesis outlined an extensive literature review and analysis of offshore and marine safety from the perspective of fire and/or explosion accidents. It analysed various causes of fire and/or explosion accidents and proposed a series of countermeasures with respect to different causes. The impact of the cryogenic temperature of LNG on steel structure during its accidental leakage has not been extensively studied. This study modelled an LNG pool formation and the impact of cryogenic temperature on a structural material during an accidental release of LNG. The study confirmed that an instantaneous LNG pool formation does cause immediate failure, however, this may significantly minimise design life of the structure and due attention is needed throughout its service life particularly in the spilled area. Literature review showed that minor leaks occur frequently, and they are often overlooked assuming that they are inconsequential. However, in the case of LNG, it can be too simple to ignore small leak due to the potentiality of causing suitable scenario for fire and explosion event upon rapid vaporisation after the leakage. This study proposed a novel technique for modelling fugitive leakage of LNG in a processing facility. The developed methodology is applied considering three different degrees of congestion and revealed that higher congestion levels present higher flammable hazards than the lower levels of congestion within the acceptable congestion level. As fire is the main cause of accident in oil and gas processing facilities, this study proposed a novel methodology for modelling fire impact assessment in a typical FLNG processing facility using Computational Fluid Dynamics (CFD). Three most credible fire accident scenarios were chosen from among various fire scenarios considered in the FLNG facility. It is found that the scenario in the Mixed Refrigerant Module in the liquefaction process presents the highest risk of fire to both on-board personnel and assets. In a complex processing facility, there is a high likelihood of occurrence of transitional scenarios such as hydrocarbon release, fire, explosion and dispersion of combustion products. Finally, this study modelled potential transitional events and their integrated impact during an accidental release of LNG. This study revealed that in a complex processing facility, transition of events is highly possible, and the impact of such events can be more severe than that of the individual event. This study serves as a comprehensive source of knowledge and technique on which to model various accident scenarios. The study of these scenarios assists in better understanding of accident causation and improves design to prevent causation of such events. The study also provides a practical approach to design safety measure to control and mitigate hazards when prevention is challenging. This thesis will serve as a guiding book to better design of processing facilities and safety measures for a complex processing facility.

Background: Studies have found that several individual and organizational factors influence the occurrence of accidents. Researchers have also highlighted the need for the development of new accident models. The main aim of this study was to assess the contributing causes of occupational accidents using a questionnaire. Methods: A total of 365 managers and employees participated in the current study from 9 manufacturing companies located in the northwest of Iran. A questionnaire comprising 100 accident causes was used to gather the required data for this study. Results: Exploratory factor analysis (EFA) yielded 14 factors contributing to accidents, and confirmatory factor analysis found satisfactory fit indices for the EFA model. The participants reported unsafe acts as the most important factor for the occurrence of accidents. A t-test revealed that participants who received safety training experienced fewer occupational accidents than other respondents. The one-way analysis of variance showed that the companies significantly differed in the perception of accident causes. Structural equation modeling indicated that organizational-managerial factors had a larger effect on individual factors than external factors. Conclusion: The results indicate that providing safety training is necessary to control the employees' unsafe acts. Managers of the companies should pay special attention to the organizational factors affecting the occurrence of accidents. The findings of this study might help the managers to develop more targeted countermeasures for reducing occupational accidents.

No advance in navigation has yet to prevent the occurrence of accidents (incidents are always implied when we discuss accidents) at sea. At the same time, advances in accident models are possible, and may provide the basis for investigations and analyses to help prevent future adverse events and improve the safety of marine transport systems. In such complex socio-technical systems models that treat accidents as the result of a chain or sequence of events are used most commonly. Such models are well suited to damage caused by failure of physical components in relatively simple systems. Although these often include methods for modeling human error, they do not cover broader aspects related to the management of the organization using the means of transport itself (shipowners) nor errors that may occur in the design phase. In particular, they do not cover changes in the systems over time. The paper presents accident investigation approaches and uses a modified causal model to analyze an incident that occurred in January 2019 on the city ferry in Świnoujście. The results of the analysis were used to provide guidelines for increasing safety at the crossing and to evaluate the accident analysis model used. Additionally, incidentally, through the study of this case we uncovered a problem in communication among stakeholders that unnecessarily complicates the models for the models for the improvement of safety.

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.

The Department of Energy`s Hanford Tank Waste Remediation system poses a significant challenge for hazard management because of the uncertainty that surrounds many of the variables that must be considered in decisions on safety and control strategies. As a result, site managers must often operate under excessively conservative and expensive assumptions. This report describes a systematic approach to quantifying the uncertainties surrounding the critical parameters in control decisions (e.g., condition of the tanks, kinds of wastes, types of possible accidents) through the use of expert elicitation methods. The results of the elicitations would then be used to build a decision support system and accident analysis model that would allow managers to see how different control strategies would affect the cost and safety of a facility configuration.

Dynamic safety analysis of process systems using nonlinear and non-sequential accident model

Modelling and analysis of unsafe acts in coal mine gas explosion accidents based on network theory

The comprehensive review of research literature in accordance with the implementations of the accident causation models enables knowledge incomes for the contributing authors towards the advancement of the accident causation methodologies for administering the accidents' causal factors. For obtaining this objective, the systematic literature review meta‐analysis are developed by evaluating the parameter indicator linked to the most utilized causation methods in addressing the contributing factors in process industries. The methodological review process called preferred reporting items for systematic reviews and meta‐analyses (PRISMA) is implemented in this paper due to its capability to integrate both systematic reviews and meta‐analysis. This systematic literature review deeply investigates 149 papers by a thorough reading from the filtered 387 manuscripts published in the academic SCOPUS databases during 1980–2020. Additionally, the statistical analysis t test is performed to identify the homogeneity of literature papers resulting from research conclusions. The suggested future improvement in the domain of accident causation models is the advancement of administering the accident causal factors' values of correlations and interdependencies. While particularly for quantified accident causation models, the adoption of uncertainty analysis needs to further developed as a research suggestion in the future.

Piauí ranks among the states with the largest honey production in Brazil, with a growing number of beekeepers concentrated especially in the semi-arid region. Despite its socioeconomic relevance, concerns arise regarding the increased risk of accidents involving Africanized bees, particularly when considering the precarious medical care provided to victims of venomous animal accidents in inland cities. The objective was to analyze the relationship between honey production in the state and the spatial distribution of bee-related accidents, as well as to describe their epidemiological characteristics. The study is exploratory-retrospective, using data available from the IBGE Automatic Recovery System and the Piauí State Health Department, covering the period from 2014 to 2023. The variables analyzed regarding accident characteristics were age group, sex, occupation, sting location, clinical manifestation, and final case classification. A total of 5,984 bee-related accidents were recorded in 176 municipalities of Piauí, representing 10.4% of venomous animal accidents in the state, with 13 deaths and an average lethality rate of 0.22%. Although a strong correlation was observed between the occurrence of bee accidents and honey production (r=0.85), there was no higher incidence of accidents among the state's main production hubs, possibly due to greater professionalization of beekeeping in these municipalities. Accidents predominantly occurred in urban areas, involving adult men, which indicates the presence of established colonies or transient swarms in the urban environment. It is concluded that there is no relationship between beekeeping production and the occurrence of accidents in Piauí.

PURPOSES : Although numerous researches have been studied to reveal accident causations for road intersections, there are still many research gaps for road segments. It is mainly because of difficulty of data and lack of analytical method. This study aims to study accident causations for rural road segments and develop accident modification factors for safety evaluation. The accident modification factors can be used to improve road safety. METHODS : Methods for developing AMF are diverse. This study developed AMFs using accident prediction models and selected explanatory variables from the accident models. In order to select final AMFs, three different methods were applied in the study. RESULTS : As a result of the study, many AMFs such as horizontal curves or vertical curves were developed and explained the meanings of the results. CONCLUSIONS : This study introduced meaningful methods for developing significant AMFs and also showed several AMFs. It is expected that traffic or road engineers will be able to use the AMFs to improve road segment safety.

Accident models provide a conceptual representation of accident causation. They have been applied to environments that have been exposed to poisonous or dangerous substances that are hazardous in nature. The home environment refers to the indoor space with respect to the physical processes the of indoor climate, e.g., temperature change, which are not hazardous in general. However, it can be hazardous when the physical process is in some states, e.g., a state of temperature that can cause heat stroke. If directly applying accident models in such a case, the physical processes are missing. To overcome this problem, this paper proposes an accident model by extending the state-of-the-art accident model, i.e., Systems-Theoretic Accident Model and Process (STAMP) with considering physical processes. Then, to identify causes of abnormal system behaviors that result in physical process anomalies, a hazard analysis technique called System-Theoretic Process Analysis (STPA) is tailored and applied to a smart home system for indoor temperature adjustment. The analytical results are documented by a proposed landscape genealogical layout documentation. A comparison with results by applying the original STPA was made, which demonstrates the effectiveness of the tailored STPA to apply in identifying causes in our case.

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

The number of cases of train accidents in Indonesia continues to be large in the past few years. This paper analyzes train accident cases in Indonesia using a socio-technical system approach accident model called AcciMap, developed by Jens Rasmussen. The source of data comes from train accident investigation reports from 2015 to 2021 published by the National Transportation Safety Committee. This study uses a qualitative approach with thematic analysis to identify contributing factors and their interactions within the reports. Based on the socio-technical system approach, there are roles and contributions from the five levels of the socio-technical railway transportation system in Indonesia that interact with each other in the occurrence of accidents, including the railway regulatory bodies; other organizations; management of railway service company; processes and physical actions by staff; and condition of equipment and environment. This study has shown that the AcciMap methodology can provide a comprehensive view of the problems and their interactions across the socio-technical levels that influence each other in the occurrence of train accidents in Indonesia. The results also suggest that the socio-technical system approach is expected to be applied in the accident investigation process for the related industry to get more comprehensive insights into the accident.

This study develops a probabilistic dynamic risk assessment framework for grounding and collision/contact accidents in narrow waterways by integrating the Human Factors Analysis and Classification System for Passenger Vessels (HFACS-PV) with Bayesian Networks (BN). Marine accident reports from the Dover Strait (2004–2020) were systematically analysed to identify human, organisational, technical, and environmental risk factors, which were subsequently structured into a Bayesian Network to model their interdependencies and dynamic influence on accident occurrence. Conditional probability tables were derived from accident data and supplemented through structured expert elicitation. The resulting model enables real-time inference and predictive risk estimation under evolving operational conditions. Model performance was evaluated using detailed grounding and collision case studies, demonstrating its capability to replicate accident evolution and quantify the contribution of key causal factors. The results indicate that unsafe acts, particularly decision-based and perceptual errors, combined with deficiencies in voyage planning, supervision, and situational awareness, dominate accident causation in the Dover Strait. The proposed framework provides a quantitative decision-support tool for vessel traffic services and maritime operators, supporting proactive risk mitigation and safety optimisation in high-density and constrained navigational environments. • Analysis of Dover Strait grounding and collision accidents using HFACS to identify human and organisational causes. • A novel dynamic risk model enables real-time assessment of collision, contact, and grounding risks for operators. • The model supports risk control measures and optimises best practices to improve safety in narrow waterways.