Incident Causation Research Articles

How Do We Analyze the Accident Causation of Shield Construction of Water Conveyance Tunnels? A Method Based on the N-K Model and Complex Network

In the construction of water conveyance tunnels with the shield method, accidents have occurred from time to time, such as collapses and explosions, and it is of practical significance to explore the cause mechanism of the accident. However, previous research has not considered the effects of dependence between risks on the risk spread. In response, we propose a method based on the Natural Killing Model (the N-K Model) and complex network theory to analyze the cause of shield construction accidents in water conveyance tunnels. By deeply exploring the transmission mechanism and action intensity between system risks, this method can scientifically clarify the accident cause mechanism and provide support for engineering construction safety management. The method constructs a risk index system. Secondly, we introduce the N-K model to reveal the risk coupling mechanism. Then, based on complex network theory, we construct the incident causation model and revise the node’s centrality with the coupling value. Finally, the network topology parameters are calculated to quantitatively describe the causal characteristics of accidents, revealing the risk evolution process and critical causes. The research results indicate that the key causes of accidents are failure to construct according to regulations, inadequate emergency measures, poor ability of judgment and decision-making, and insufficient understanding of abnormal situations. The front end of critical links is subject to human or management risks and should be carefully controlled during construction.

Dynamic risk assessment of natural gas transmission pipelines with LSTM networks and historical failure data

In the realm of energy infrastructure, ensuring the security of gas transmission pipelines is critical. This research introduces an advanced dynamic risk assessment framework that leverages the predictive capabilities of LSTM networks, presenting an improvement over conventional failure prediction models. Unlike traditional approaches that rely on averaging historical failure records, this framework dynamically processes historical pipeline failure incidents into sequential time series analysis, facilitating and improving the accuracy of the current failure rate. The model refines the failure rate estimation for individual pipelines by incorporating unique characteristics and modification factors, resulting in a highly precise failure likelihood estimation. Additionally, this study introduces a quantifiable linkage between mortality risk and the fatality probit value across various accident scenarios, enhancing consequence evaluation. A sensitivity analysis is then performed to assess the impact of various input parameters on the model's performance. The practical application of the model on a U.S. pipeline confirms its effectiveness. This proposed methodology substantially enhances the understanding of incident causation in gas pipeline systems, paving the way for superior safety management strategies. It is instrumental in enhancing pipeline safety, refining infrastructure planning, and optimizing safety resource allocation. The methodology offers benefits for pipeline operators, industry professionals, and regulatory agencies, contributing to improved operational safety and resource management in the pipeline industry.

Application of Functional Resonance Analysis and fuzzy TOPSIS to identify and prioritize factors affecting newly emerging risks

Conventional safety analyses in complex systems like air separation units (ASUs) often attributed accidents to linear, deterministic causes, such as operator error. However, acknowledging the intricate interdependence of process components necessitates a shift towards recognizing the complexity of incident causation. This study proposes a novel model that integrates Function Resonance Analysis Method (FRAM) and fuzzy logic analysis to address this growing need. The model facilitates the identification of emerging risks and assesses the impact of influential factors within a mixed qualitative and quantitative framework. The FRAM method is initially employed to identify emerging risks within the ASU. Subsequently, fuzzy multi-criteria decision-making methods are utilized to establish the relationships and weightage of influential factors. Data collection encompasses semi-structured interviews, direct observation, process workflow analysis, and the involvement of a panel of engineers and operators from the investigated ASU. Utilizing FMV software for FRAM analysis, functions associated with air compression, distribution, and storage exhibit high resonance. This signifies substantial variability and a heightened potential for incidents or deviations in these functions and higher-level tasks. Furthermore, Fuzzy TOPSIS analysis reveals that education and experience emerge as the most impactful factors governing newly emerging risk. This model demonstrates significant merit for risk assessment and incident investigation. Its non-linear and dynamic nature empowers the proactive identification and examination of processes, incidents, and emerging risks before deviations or accidents occur.

An incident reporting and learning system to understand cycling incident causation in Australia: A 12-month implementation of CRIT

The benefits of cycling are well-recognised, however, safety remains a key barrier to cycling participation. The knowledge base on cyclist safety is limited as existing data systems are inadequate for understanding cycling incident causation and many incidents go unreported. This article presents the findings from a 12-month implementation of a new cyclist incident reporting and learning system—the Cyclist Reporting of Incidents Tool (CRIT). A total of 347 cyclists participated in the present study by providing their cycling participation data and cycling incident data, including their perceptions of the contributory factors involved in their crashes and near miss incidents. The analysis included 135 cycling incidents (22 crashes, 113 near miss incidents) from 1962 hours cycled. There was an overall incident rate of 68.8 incidents per 1000 hours cycled, with a crash rate of 11.2 and near miss incident rate of 57.5 incidents per 1000 hours cycled. Cyclists frequently reported that factors relating to road users (e.g., cyclist and driver behaviour), and equipment, environment, and surroundings (e.g., road and cyclist infrastructure) contributed to their cycling incidents. Factors relating to the broader transport system (e.g., infrastructure maintenance and repairs) were also reported. Finally, there were commonalities in the contributory factors reported for crashes and those for near miss incidents, highlighting the importance of understanding near miss incident causation in cyclist safety. We recommend that similar incident reporting and learning systems be implemented for all road users, especially for vulnerable road users whose incidents may not otherwise be captured in data systems.

Evaluation of error across natural gas pipeline incidents.

This paper demonstrates how qualitative analysis can be a novel means of investigating theories of error and causation in natural gas pipeline incidents. Qualitative analysis offers unique opportunities to understand process, interactions, and the role of context in identifying active error and latent conditions in incident causation. Through the coding of text from 24 onshore natural gas pipeline incident reports on leaks and explosions in the United States and Canada, our findings reveal a proportion of active and latent errors consistent with other hazardous infrastructure contexts (roughly 3:1 latent-active ratio across 817 coded errors). These findings underscore the robustness of extant error theory and support the argument for documenting multiple, connected causes of disaster in aggregate. Conclusions highlight the utility of in-depth case analyses and critique present pipeline incident database aggregation. Our interpretation provides a means to convey complex causation in aggregate form thus enabling more nuanced future qualitative and qualitative analyses.

Enhancing fire service incident investigation – Translating findings into improved outcomes using PIAM

Since 1927 more than 242 formal inquiries and reviews into Australian natural disasters, and 62 international post-incident investigations following firefighter fatalities or injuries during wildfire entrapment and burnover have been completed. Despite the significant number of completed inquires, evidence suggests emergency services continue to repeat mistakes of the past when preparing for, preventing, responding to, and recovering from disasters. Where ongoing mistakes occur, the interventions themselves (including analysis methods) must be questioned as the significant cost of repeated inquiries may divert funding from improving both the capabilities and capacities of the emergency services being investigated. The objective of this paper is to work towards addressing limitations of investigative processes within the context of Australian fire and emergency services. It does this by building upon the Incident Causation Analysis Model (ICAM), and proposes the resultant PESTLE Incident Analysis Model (PIAM). The PIAM is designed to enhance analysis of adverse incidents, and to assist emergency services both communicate and implement recommendations from post-incident investigations and analysis. The PIAM is validated using a historical firefighter entrapment. In doing so this paper demonstrates the suitability of the PIAM technique for both emergency service and more traditional occupational safety investigations, making the critical connection between research and practice that is essential within emergency services worldwide.

A network analytical framework to analyze infrastructure damage based on earthquake cascades: A study of earthquake cases in Japan

Earthquakes can result in disasters affecting infrastructure and assets that are exposed and vulnerable and thus prone to damage. Considering earthquake cascades, we can reveal the immediate and intermediate effects of earthquakes causing infrastructure damage. In this study, we propose a network analytical modeling approach to analyze earthquake cascades. The analytical framework integrates incident causation modeling and probabilistic network analysis. The linear effects from earthquakes are delineated in incident chains to represent the consequences of earthquakes to infrastructure; the incident chains are then remodeled topologically by the network theory to reveal the nonlinear cascading effects of earthquakes to infrastructure. The network of earthquakes’ effects and intermediate effects is then structurally analyzed by probabilistic network analysis that uncovers the unifying principles and statistical topology of earthquake cascades to infrastructure. The results show that earthquakes can directly cause significant damage to infrastructure and indirectly cascade the effects to infrastructure damage via intermediate effects; each effect or intermediate effects in earthquake cascades have their own role in leading to infrastructure damage. The critical infrastructure damage due to earthquakes and critical intermediate effects in propagating earthquake cascades are revealed in this study by the network analysis of network density, degree centrality, closeness centrality, betweenness centrality, and cascade quantification. To reduce infrastructure damage due to earthquake cascades, we suggest tackling the directed interdependent intermediate effects to minimize the risks associated with earthquakes to infrastructure damage. The network analytical framework in this study can be used to understand the propagation damage of earthquakes on infrastructure; thus, our work can provide valuable insight into earthquake preparedness and emergency management.

A qualitative analysis of crane safety incident causation in the Australian construction industry

Crane safety incidents in the construction industry are persistent and have serious consequences. The research explored causal and contributing factors to crane safety incidents in the Australian construction industry. Key informants were purposefully selected because of their industry experience and knowledge of crane operations in the Australian construction industry. Informants participated in focus groups or interviews, in which the factors that cause or contribute to crane safety incidents in construction were explored. Data was systematically analysed using the framework method of qualitative data analysis. A total of 77 causal/contributing factors were identified in the analysis, which were found to operate at multiple levels within the work system relating to the use of cranes in the construction industry. The analysis also revealed that these factors interact with each other in complex ways within and between levels in the work system. Understanding these interactions can inform the selection and implementation of ‘upstream’ prevention measures for crane safety incident in the construction industry.

Factors leading to unsafe behavior in the twenty first century workplace: a review

Work-related injuries, diseases and fatalities have received considerable attention by researchers and have led to a greater awareness about incident causation in the workplace. Most accidents and injuries emanating from workplaces are attributed to workers’ unsafe behaviors, which are also a reflection of system deficiency and hazardous work-environment. The purpose of this review was to investigate the factors contributing to workers’ unsafe behaviors. A review of 70 major empirical studies on unsafe behaviors was conducted within occupational safety and health research, and thereafter clustered into eight job domains namely: construction, healthcare, informal sector enterprises, manufacturing, mining, energy, agriculture, and multi-dimensional context. The results across these domains were reported and compared, along with their consequences and solutions. The study found that lack of adequate knowledge on safety and health, violation of safety rules, work pressure, stress and non-use of protective equipment were the main factors of unsafe behaviors. Several major solutions to these behaviors were highlighted for consideration by practitioners and policy developers. The outcomes from this review will assist safety managers in understanding how to motivate employees/workers to engage in proper safety practices.

Lost in translation: the validity of a systemic accident analysis method embedded in an incident reporting software tool

ABSTRACTDespite the proposed advantages of systems accident analysis (SAA) methods for understanding incident causation, they have not been widely adopted by practitioners. The aim of this study was to evaluate the criterion-referenced validity of an SAA method embedded within an incident reporting software tool. Thirteen practitioners used the tool to collect and analyse incident data within their organisation. The incident data were then analysed by researchers experienced in using the SAA method. Overall, there were low levels of agreement between participants and researchers regarding the identification and classification of factors and relationships. The findings indicate the systems thinking principles underpinning the SAA method may have been ‘lost in translation’, in that participants often identified only one or two factors and showed a poor understanding of how to identify relationships between factors. The methodological developments required to ensure that practitioners can validly apply the SAA method are discussed.

Perceptions of Older People Regarding Their Vision and Incident Causation.

It is widely known that visual impairment (VI) is a risk factor for falls, but patients or their eye care practitioners may not recognize other kinds of incidents as being problematic because of their vision. Consequently, older people with VI may have unmet needs for advice on how to carry out activities of daily living safely. Therefore, the purpose of this study was to understand whether older people with VI consider their vision as a causative factor of incidents they experience and their perceptions regarding the prevention of future incidents. If sample size permitted, a secondary aim was to evaluate whether quantitative findings supported their perceptions. The study design was a prospective cohort study evaluating injurious and damaging incidents and related near misses using open questions in a written 2-weekly large-print diary with active follow-up over 8 weeks in older people (>60 years, n = 80) with and without VI. Baseline measures included habitual binocular visual acuity, contrast sensitivity, visual fields, 3-m walk test, and Short Form 12 physical and mental component scores. Participants' diary entries were coded. Factor analysis and binary logistic analysis were used to investigate whether baseline measures were predictive of incident occurrence. Risk and preventative factors identified were compared. Participants perceived that their vision was implicated in bump and fall incidents. Quantitative analysis indicated that contrast sensitivity and fitness were significant predictors of incident occurrence. Six vision-related and five non-vision-related causative factors were identified by participants as contributing factors. Participants frequently stated "don't know" when asked to identify solutions to prevent incident recurrence. Participants had unmet needs for advice in relation to incident prevention. It would be prudent for eye care practitioners to raise incident prevention in eye care consultations regardless of voiced patient concerns.

Triangulating Safety: Applying Social Media Analysis Methods to Revolutionize Patient Safety

Too many people suffer from preventable medical errors. Medical error reporting systems have already been deployed in many countries to prevent similar errors from happening again. Through such systems, millions of errors have been reported and analyzed to yield information on safety incidents. Nevertheless, there is still huge room for improvement, especially in analyzing the free text description parts of those reports. Free text entries offer richer and more detailed information on incidents, but are too often ignored or wasted due to a lack of analytical resources. Recent developments in social media analysis can be utilized to effectively address this problem. Thanks to huge advancements in computing power and algorithm design, social media companies are effectively analyzing millions of free text postings every day to extract relevant information. Their automated analysis methods can be applied to analyze medical error reports. This includes collating detailed information on incident causation and even cultural issues like power gradients among health care organizations - all information that can dramatically improve safety. Many errors are reported each day, and many are waiting to be thoroughly analyzed. Leaving them unused is neither necessary nor justifiable. Currently available computing power and analytical methods can be utilized to activate their contribution to reducing medical errors.

Demonstrating a Correlation Between the Maturity of Road Safety Practices and Road Safety Incidents

Objective: The objective of this study is to demonstrate a correlation between the maturity of a country's road safety practices and road safety incidents.Methods: Firstly, data on a number of road injuries and fatalities for 129 countries were extracted from the United Nations Global Status on Road Safety database. These data were subdivided according to road safety incident and accident causation factors and normalized based on vehicular fleet (per 1000 vehicles) and road network (per meter of paved road). Secondly, a road safety maturity model was developed based on an adaptation of the concept of process maturity modeling. The maturity of countries with respect to 10 road safety practices was determined through the identification of indicators recorded in the United Nations Global Status of Road Safety Database. Plots of normalized road safety performance of the 129 countries against their maturity scores for each road safety practice as well as an aggregation of the road safety practices were developed. An analysis of variance was done to determine the extent of the correlation between the road safety maturity of the countries and their performance. In addition, a full Bayesian analysis was done to confirm the correlation of each of the road safety practices with injuries and fatalities.Results: Regression analysis for fatalities, injuries, and combined accidents identified maturity with respect to road safety practices associated with speed limits and use of alternative modes as being the most significant predictors of traffic fatalities. A full Bayesian regression confirms that there is a correlation between the maturity of road safety practices and road safety incidents.Conclusion: Road safety practices associated with enforcement of speed limits and promotion of alternative modes are the most significant road safety practices toward which mature countries have concentrated their efforts, resulting in a lower frequency of fatalities, injury rates, and property damage accidents. The authors argue that the use of gross domestic product (GDP) as a predictor of road safety incidents suffers from the presumptive assumption that the only criterion that matters is national income, therefore erroneously predicting that richer countries all perform approximately the same in fatalities and that developing nations are at various stages, with high variability and uncertainty in prediction. It is proposed that an aggregation of individual maturity scores from road safety practices will lead to a better indicator for policy because it connects externalities (fatalities/injuries) with intrinsic factors for which policies can be oriented.

Accident causation models, management and the law

To apportion blame, and by extension liability, for an accident it is necessary to decide causality, who caused the accident and how it was caused. The same requirements apply to the preventative management of such potential accidents, except blame is assigned post-hoc, after the event, whereas preventative management is essentially proactive and obviates the need for blame. Much thinking is based on the notion that there is a single root cause of an incident, the most important cause and therefore the one pointing at liability as well as determining the main target for prevention. This is embedded in the idea that incident causation is linear and deterministic, that there are clear sequences of causes going back to a root cause. This way of thinking has proved very successful and its preventative application may be regarded as reducing the number of (potential) accidents by 80%. Most of these 80% accidents are personal; the development and use of the Swiss Cheese model, aimed also at process incidents, has led to a further reduction of possibly 80% of the remaining potential incidents, now covering some 96% in total. Such models are still deterministic, but non-linear in their causal effects. The remaining 4% of possible incidents, especially complex and major process accidents, unfortunately appears to be much more intractable. The proposal is that these incidents have a causal structure that is both non-linear and non-deterministic, being inherently probabilistic. This has consequences for the management and prevention of such incidents, because of their complexity, but also for the legal approach, that has to confront non-deterministic and non-linear causation. The legal viewpoint is made more complex because, in hindsight, such incidents still appear to be simple, linear and deterministic.

Occupational Injury and Fatality Investigations

The forensic evaluation of trauma in occupational injuries and fatalities can provide the benefit of a more thorough analysis of incident causation. Forensic nursing science applied during workplace investigations can assist investigators to determine otherwise unknown crucial aspects of the incident circumstances that are important to event reconstruction, the enforcement of occupational health and safety requirements, and the direction of workplace prevention initiatives. Currently, a medical and forensic medical knowledge gap exists in the subject-matter expertise associated with occupational accident investigations. This gap can be bridged with the integration of forensic nursing in the investigation of workplace fatalities and serious injuries.

A case study analysis of fatal incidents involving excavators in the Australian construction industry

Purpose – The paper analyses the nature and causes of fatal incidents involving excavators occurring in the Australian construction industry. A three-level incident causation model developed by researchers at Loughborough University forms the theoretical framework for this analysis, which seeks to identify immediate circumstances, shaping factors and originating influences in selected incidents. Design/methodology/approach – Case study incidents were identified from the National Coronial Information System database. These incidents were subjected to content analysis to identify causal factors. Findings – Ten cases were analysed in total. In all of these cases immediate circumstances could be identified. These included the use of unsafe work methods and the condition, suitability or useability of plant. In several cases shaping factors, such as communication between work-team members and the design of work processes, were identified as likely contributors to the incidents. In none of the cases could originating influences be identified. Research limitations/implications – The research was limited by the relatively small number of cases for which detailed investigation reports were available and the fact that, for the most part, the reports focused on the immediate circumstances surrounding the incidents. Practical implications – The circumstances of the fatal incidents in Australia are similar to those reported in the UK and the USA and the identified causes have known safety solutions. The persistence of these incidents in the Australian construction industry suggests that there may be underlying reasons why known safety solutions are not implemented. Further in-depth analysis of incident causes may help to identify organisational and/or cultural causes of incidents involving excavators. Originality/value – The analysis provides a more detailed qualitative analysis of the causes of fatal incidents involving excavators than would is possible using national compensation data, which restricts analysis to a classification of the mechanism and agency of injury.

The how and why of plant‐related fatalities in the Australian construction industry

Purpose – Drawing on the findings of coronial investigations, this research aimed to investigate the circumstances and causes of fatal incidents involving plant in the Australian construction industry. The analysis sought to provide greater insight into how and why fatal incidents occur and to inform recommendations for the prevention of fatal incidents involving plant.Design/methodology/approach – Fatal incidents involving plant were identified from the National Coronial Information System. In each case, the decedent was a construction worker and the incident occurred at a construction worksite. A systemic incident causation model developed by Loughborough University informed the identification of originating influences, shaping factors and immediate circumstances in each incident.Findings – Most of the incidents involved excavators, trucks and cranes, and different classifications of plant were associated with different types of incident. The most common incident types involved people being run over by ...

Dynamic Fuzzy Logic Model for Risk Assessment of Marine Crude Oil Transportation

As a scarce strategic resource, crude oil is closely intertwined with national strategies, global policies, international relationships, and national competence. Unbalanced demand and production of crude oil lead to huge amounts of import and export activities. Marine transportation accounts for about 80% of the total oil transport because of the low cost. However, it also incurs an extremely high risk in doing so. Therefore, it is significantly important to assess and control risk effectively in crude oil transportation. In risk assessment, uncertainty evaluation becomes a key variable that cannot be neglected. Previous cases show that fuzzy logic can be applied to manage uncertainty. However, the use of fuzzy logic usually encounters two difficulties: determination of the membership functions and establishment of the reference machine. This paper presents a dynamic fuzzy logic model (DFLM) that can improve the stated problems. The DFLM is developed from fuzzy theory and an incident causation model. The model is examined with a hypothetical case. Preliminary results show that the model reduces the time of risk assessment and increases the reliability of evaluation.

Factor interaction influences on human performance in air traffic control: The need for a multifactorial model

In air traffic management (ATM) knowledge of the impact of human factors on performance is critical to address safety incidents. Previous research has largely focused on the effects of single factors on performance which has resulted in a comprehensive understanding of single factor effects. In current control environments however, the residual threats for incidents often result from the interaction of multiple human factors and the resulting cumulative impact on performance. This research uses a literature review, an analysis of over 400 European aviation incident reports and finally a survey of ATM professionals to assess the need for a multifactorial model of performance. Literature findings suggest that Human Factors approaches are fundamentally single-factor in nature, which is out of step with real ATM working contexts. An incident report analysis, supported by a survey of air traffic experts, suggests that multiple factor incident causation exists. This discrepancy suggests the need for a new approach to looking at how incidents occur, and their factors managed, on a day-to-day basis. The proposed solution is a multifactorial model of human performance.

A systematic approach towards accident analysis and prevention

A systematic approach towards accident analysis and prevention has been developed. It relies on system theory as an incident causation model, and adopts a hybrid model for identifying elements of the safety management system. PDCA (Plan-Do-Check-Act) process, commonly practiced in business for quality control, has been applied to defining components of the system. Using the experts’ judgment, accident data and their reported causes are correlated to the defined components, with RBI (risk-based inspection) defined consequence scores as weighting factors. The application of this approach allows users such as governments and companies to identify and prioritize among causes of accidents and near-misses in the petrochemical industry. A case study using the accident data of Yeosu petrochemical complex from 1990 to 2004 has been applied to illustrate insights readily obtainable by using the developed analysis technique. The results suggest comprehensive identification and ranking of accident causes for effective prevention of accidents in the future.