A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors

Work-related head injury and industry sectors in Finland: causes and circumstances

OBJECTIVE

Despite the continuous development of occupational safety, the prevalence of work-related head injuries is excessive. To promote prevention, we conducted a study evaluating the risks and pathways that precede head injuries in different economic activity sectors.

METHODS

In Finland, more than 90% of employees are covered by inclusive statutory workers' compensation. We obtained data on occupational head injuries in 2010-2017 from an insurance company database. The European Statistics on Accidents at Work (ESAW) variables represented the characteristics of the accidents and the injury. We analysed the risk factors, contributing events and injury mechanisms in 20 industry sectors, based on the Statistical Classification of Economic Activities in the European Community (NACE).

RESULTS

In the 32,898 cases, the most commonly affected area was the eyes (49.6%). The highest incidence of head injuries was in construction (15.7 per 1000 insurance years). Construction, manufacturing, and human health and social work activities stood out due to their distinctive ESAW category counts. 'Working with hand-held tools' [risk ratio (RR) 2.23, 95% confidence interval (CI) 2.14-2.32] in construction and 'operating machines' (RR 3.32, 95% CI 3.01-3.66) and 'working with hand-held tools' (1.99, 1.91-2.07) in manufacturing predicted head injury. The risk related to parameters of violence and threats in health and social work activities was nearly ninefold the risk of other sectors.

CONCLUSION

The risks and pathways preceding head injuries varied considerably. The highest head injury rates were in construction and manufacturing. Violence emerged as a major risk factor in human health and social work activities.

Development and Initial Validation of the Safety Training Engagement Scale (STE-S)

Safety training promotes safety at work, in particular through the use of engaging methods. This study introduces a newly developed measure of individual engagement in safety training, and aims to analyze the psychometric proprieties of the scale. The safety training engagement scale (STE) consists of five items pertaining to the trainee’s dedication and absorption in a safety training session. Two studies are carried out to analyze the validity of the scale. The first study focuses on the construct (internal) validity, to examine the scale’s internal consistency and dimensional structure. The second study seeks to provide further evidence for construct validity by testing the external validity of the scale. The sample consists of 913 (study 1) and 133 (study 2) participants in safety training programs in the field of the chemical industry who were invited to fill the STE scale after attending a safety training course. The results provide support to affirm the validity and reliability of the scale. The discussion describes the implication and the limitations of using the STE scale in practical safety training programs, and outlines recommendations for research to improve the scale’s robustness.

The Use of a Fault Tree Analysis (FTA) in the Operator Reliability Assessment of the Critical Infrastructure on the Example of Water Supply System

Background: Specialist literature indicates a large share of the human factor among the causes of failure of technical systems at the level of 70 to 90%, which depends on the sector studied. The collective water supply system is an anthropotechnical system, i.e., it is a complex connection between man and the technical system resulting from the deliberate influence of man on the technical system. Methods: The work presents an assessment of operator reliability of a selected water treatment process based on the fault tree analysis (FTA). Elementary events are determined by the operator’s error probability. Results: A failure tree was prepared for the peak event of the filter station failure, resulting from an operator’s error during the filter washing procedure. The probability of a peak event occurring is 0.0580. Conclusions: The developed fault tree allows for the identification of elementary events leading to an emergency event. The operator fulfills its task of maintaining the continuity of water treatment.

Determining Role of Human Factors in Maritime Transportation Accidents by Fuzzy Fault Tree Analysis (FFTA)

Safety has been a primary concern in every industry. It includes system, personnel, environmental safety, etc. Maritime transportation safety is of the utmost importance because a lot of economic and environmental damage has been caused by ship-related accidents. The majority of these accidents have resulted from human factors. For the analysis of accidents and future safety, various accident models have been created. In this study, human-based errors are analyzed and quantified by using the fuzzy fault tree analysis, which helps calculate the failure probability of the causes. A real-life case of a chemical tanker Key Bora was studied and analyzed, which happened on 28 March 2020, at Kyleakin Pier, Isle of Skye, Scotland. The ship’s hull was seriously damaged and was flooded. According to the analysis, two main human factors that contributed the most to the occurrence of this accident were found. These incidents can be avoided by ensuring proper measures are followed, and the results can be used as guidelines for future marine accident investigations and safety.

Quantitative Ship Collision Frequency Estimation Models: A Review

Ship collisions are one of the most common types of maritime accidents. Assessing the frequency and probability of ship collisions is of great importance as it provides a cost-effective and practical way to mitigate risk. In this paper, we present a review of quantitative ship collision frequency estimation models for waterway risk assessment, accompanied by a classification of the models and a description of their main modelling characteristics. Models addressing the macroscopic perspective in the estimation of ship collision frequency on waterways are reviewed in this paper with a total of 29 models. We extend the existing classification methodology and group the collected models accordingly. Special attention is given to the criteria used to detect potential ship collision candidates, as well as to causation probability and the correlation of models with real ship collision statistics. Limitations of the existing models and future improvement possibilities are discussed. The paper can be used as a guide to understanding current achievements in this field.

A data-driven feature learning approach based on Copula-Bayesian Network and its application in comparative investigation on risky lane-changing and car-following maneuvers

The era of 'Big Data' provides opportunities for researchers to have deep insights into traffic safety. By taking advantages of 'Big Data', this study proposes a data-driven method to develop a Copula-Bayesian Network (Copula-BN) using a large-scale naturalistic driving dataset with multiple features. The Copula-BN is able to explain the causality of a risky driving maneuver. As compared with conventional BNs, the Copula-BN developed in this study has the following advantages: the Copula-BN 1. Has a more rational and explainable structure; 2. Is less likely to be over-fitting and can attain more satisfactory prediction performance; and 3. Can handle not only discrete but also continuous features. In terms of technical innovations, Shapley Additive Explanation (SHAP) is used for feature selection, while Gaussian Copula function is employed to build the dependency structure of the Copula-BN. As for applications, the Copula-BNs are used to investigate the causality of risky lane-changing (LC) and car-following (CF) maneuvers, upon which the comparisons are made between the two essential but risky driving maneuvers. In this study, the Copula-BNs are developed based on the Second Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) database. Upon network evaluation, the Copula-BNs for both risky LC and CF maneuvers demonstrate satisfactory structure performance and promising prediction performance. Feature inferences are conducted based on the Copula-BNs to respectively illustrate the causation of the two risky maneuvers. Several interesting findings related to features' contribution are discussed in this paper. To a certain extent, the Copula-BN developed using the data-driven method makes a trade-off between prediction and causality within the 'Big Data'. The comparison between risky LC and CF maneuvers also provides a valuable reference for crash risk evaluation, road safety policy-making, etc. In the future, the achievements of this study could be applied in Advanced Driver-Assistance System (ADAS) and accident diagnosis system to enhance road traffic safety.

Why the government should be blamed for road safety

The government plays an important role in road safety. However, the effectiveness of the government in the context of road traffic accidents (RTAs) is rarely measured quantitatively. This study aims to quantitatively examine the effects of government regulation on human and organizational factors. A contributing factors classification framework of RTAs is presented based on the human factors analysis and classification system, one of the most popular systems approaches. A total of 405 major RTAs was collected over a 20-year period (1997-2017) in China and analyzed through the structural equation model. The results lead to two main conclusions: the frequency of inadequate regulation, which has reached 343, is the highest frequency among all contributing factors; government regulation exhibits significant effects on organizational influences, unsafe supervision and unsafe behaviors. These findings provide a new perspective for accident prevention that can be initiated by the government in policy-making and regulatory activities.

Critical Hazards Identification and Prevention of Cascading Escalator Accidents at Metro Rail Transit Stations

Escalator accidents not only happen frequently but also have cascading effects. The purpose of this study is to block the formation of cascading accident networks by identifying and preventing critical hazards. A modified five-step task-driven method (FTDM) is proposed to break down passenger-related cascading escalator accidents. Three complex network parameters in complex network theory are utilized to identify critical and non-critical Risk Passenger Behavior (RPB) hazards and Other Hazards related with Risk Passenger Behavior (OH-RPB) in accident chains. A total of 327 accidents that occurred in the Beijing metro rail transit (MRT) stations were used for case studies. The results are consistent in critical and non-critical RPB and OH-RPB and prove that through combination of FTDM accident investigation model and complex network analysis method, critical and non-critical RPB and OH-RPB in a complicated cascading hazards network can be identified. Prevention of critical RPB can block the formation of cascading accident networks. The method not only can be used by safety manager to make the corresponding preventive measures according to the results in daily management but also the findings can guide the allocation of limited preventive resources to critical hazards rather than non-critical hazards. Moreover, the defects of management plan and product design can be re-examined according to the research results.

Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network

Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow-ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.

Application and modification of the Tripod Beta method for analyzing the causes of oil and gas industry accidents

Background. Understanding the causes of accidents plays a major role in learning from accidents and developing accident prevention and control strategies. Objective. This study aimed at application and modification of the Tripod Beta method for analyzing accident causes in the oil and gas industries. Materials and methods. A total of 68 accidents occurring in the oil and gas industries during 2005-2016 were analyzed. For this purpose, we used the Tripod Beta method and modified it using Reason's Swiss cheese model and analysis accident results. Results. The main causes that have been ignored in the Tripod Beta method were supervision factors involved in 66% of the accidents (underlying causes) and unsafe conditions that contributed to 55% of accidents (immediate causes). The former was incorporated as a sublayer of the underlying cause and the latter as a sublayer of immediate cause to the modified Tripod Beta method. Conclusions. The results of the present study added to the knowledge on the causes of accidents. These results can help increase the capabilities of the Tripod Beta method for analyzing accident causes, such as supervision factors and unsafe conditions, which have been ignored in analyses performed using the Tripod Beta method.

Analysis of the severity of occupational injuries in the mining industry using a Bayesian network

OBJECTIVES

Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.

METHODS

The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents.

RESULTS

Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers’ experience had the strongest influence on the severity of accidents.

CONCLUSIONS

Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.

Half-century research developments in maritime accidents: Future directions

Over the past 50 years, research in maritime accidents has undergone a series of fundamental changes. Understanding the evolution of these changes can help maritime communities to know what has been done in the past, how maritime safety can be improved in the future, and how to reduce or eliminate the risks to ships, the lives aboard them, the cargo they carry, and the marine environment. This study conducts a comprehensive literature review on research in maritime accidents, comprising 572 papers published in 125 journals over the 50 years from 1965 to 2014. The patterns of evolution of the researchers, the journals, the disciplines involved, the research methods, the major issues and causes, and the data sources are identified, and the changes explained. We find that the main focus of research in maritime accidents has shifted over the past 50 years from naval architecture to human error, and may continue to expand into socio-economic factors. In addition, future research in maritime accidents will be multi-disciplinary, use multiple data sources, and adopt advanced research methods, to account for complex interactions between the natural environment, the development of naval technology, human behavior, and shipping market conditions.

An Interactive Model among Potential Human Risk Factors: 331 Cases of Coal Mine Roof Accidents in China

In order to explore optimal strategies for managing potential human risk factors, this paper developed an interactive model among potential human risk factors based on the development processes of accidents. This model was divided into four stages, i.e., risk latency stage, risk accumulation stage, risk explosion stage and risk residue stage. Based on this model, this paper analyzed risk management procedures and relevant personal's responsibility in each stage, and then probed into the interactive mechanism among human risk factors in three aspects, i.e., knowledge, information and communication. The validity and feasibility of the model was validated by analyzing a coal mine roof accident in China. In addition, the contribution of different functional levels' personnel in risk evolution was discussed. It showed that this model can effectively reveal the interactive mechanism of potential human risk factors, and can thus give significant insights into the development of risk management theories and practices. It also proves that the contribution of different functional levels' personnel in the model is different. This can further help practitioners design enhanced Behavioral-Based Safety (BBS) intervention approaches which can have a more sustainable and persistent impact on corporate personnel's safety behavior. Specific recommendations and suggestions are provided fundamentally for future BBS practices in the coal mine industry.

Safety of Workers in Indian Mines: Study, Analysis, and Prediction

Background

The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent.

Methods

The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc.

Results

The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed.

Conclusion

Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

Dynamic safety assessment of natural gas stations using Bayesian network

Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.

Reinforcing supply chain security through organizational and cultural tools within the intermodal rail and road industry

Purpose

The purpose of this paper is to outline the role of organizational and cultural tools to increase supply chain security within the intermodal rail and road industry. Three main research questions are set, regarding: what organizational and cultural tools are used by companies within the intermodal rail and road industry; how these tools impact on security performance; and what environmental factors trigger the use of each tool.

Design/methodology/approach

In total, 13 companies within the intermodal rail and road industry have been studied in detail through in-depth case studies.

Findings

Results suggest that organizational and cultural tools impact positively on supply chain security, by reducing collusion and both operative and planning mistakes. In particular, such tools mitigate the effect of lack of cooperation and communication between partners and of inadequate partners.

Practical implications

Results point out that the ability of organizational and cultural tools to increase supply chain security has not been fully exploited yet. Tools to mitigate the negative effects on security of inadequacy of partners are not popular or they are not considered as powerful enough, despite it has been highlighted as the most relevant causal factor of lack of security.

Originality/value

This paper introduces a thorough overview of the effects of cultural and organizational tools on supply chain security and a detailed study of these tools in the area of intermodal rail-and-road transport.

Analysis 320 coal mine accidents using structural equation modeling with unsafe conditions of the rules and regulations as exogenous variables

Mining has been historically considered as a naturally high-risk industry worldwide. Deaths caused by coal mine accidents are more than the sum of all other accidents in China. Statistics of 320 coal mine accidents in Shandong province show that all accidents contain indicators of "unsafe conditions of the rules and regulations" with a frequency of 1590, accounting for 74.3% of the total frequency of 2140. "Unsafe behaviors of the operator" is another important contributory factor, which mainly includes "operator error" and "venturing into dangerous places." A systems analysis approach was applied by using structural equation modeling (SEM) to examine the interactions between the contributory factors of coal mine accidents. The analysis of results leads to three conclusions. (i) "Unsafe conditions of the rules and regulations," affect the "unsafe behaviors of the operator," "unsafe conditions of the equipment," and "unsafe conditions of the environment." (ii) The three influencing factors of coal mine accidents (with the frequency of effect relation in descending order) are "lack of safety education and training," "rules and regulations of safety production responsibility," and "rules and regulations of supervision and inspection." (iii) The three influenced factors (with the frequency in descending order) of coal mine accidents are "venturing into dangerous places," "poor workplace environment," and "operator error."

The Evolution of Process Safety: Current Status and Future Direction

The advent of the industrial revolution in the nineteenth century increased the volume and variety of manufactured goods and enriched the quality of life for society as a whole. However, industrialization was also accompanied by new manufacturing and complex processes that brought about the use of hazardous chemicals and difficult-to-control operating conditions. Moreover, human-process-equipment interaction plus on-the-job learning resulted in further undesirable outcomes and associated consequences. These problems gave rise to many catastrophic process safety incidents that resulted in thousands of fatalities and injuries, losses of property, and environmental damages. These events led eventually to the necessity for a gradual development of a new multidisciplinary field, referred to as process safety. From its inception in the early 1970s to the current state of the art, process safety has come to represent a wide array of issues, including safety culture, process safety management systems, process safety engineering, loss prevention, risk assessment, risk management, and inherently safer technology. Governments and academic/research organizations have kept pace with regulatory programs and research initiatives, respectively. Understanding how major incidents impact regulations and contribute to industrial and academic technology development provides a firm foundation to address new challenges, and to continue applying science and engineering to develop and implement programs to keep hazardous materials within containment. Here the most significant incidents in terms of their impact on regulations and the overall development of the field of process safety are described.

Influence of Cushioning Variables in the Workplace and in the Family on the Probability of Suffering Stress

Stress at work and in the family is a very common issue in our society that generates many health-related problems. During recent years, numerous studies have sought to define the term stress, raising many contradictions that various authors have studied. Other authors have attempted to establish some criteria, in subjective and not very quantitative ways, in an attempt to reduce and even to eliminate stressors and their effects at work and in the family context. The purpose of this study was to quantify so-called cushioning variables, such as control, social support, home/work life conciliation, and even sports and leisure activities, with the purpose of, as much as possible, reducing the negative effects of stress, which seriously affects the health of workers. The study employs data from the Fifth European Working Conditions Survey, in which nearly 44,000 interviewees from 34 countries in the European Union participated. We constructed a probabilistic model based on a Bayesian network, using variables from both the workplace and the family, the aforementioned cushioning variables, as well as the variable stress. If action is taken on the above variables, then the probabilities of suffering high levels of stress may be reduced. Such action may improve the quality of life of people at work and in the family.

Early warning indicators for challenges in underground coal storage

Early warning or leading indicators are discussed for unexpected incidences in case of large-scale underground coal storage at a power plant. The experience is compared with above-ground stockpiles for which established procedures are available but where access for prevention and mitigation are much easier. It is suggested that while the explicit organization, procedures, and the general safety systems aim to provide the targeted levels of performance for the storage, representing new technology without much precedence elsewhere in the world, the extensive experience and tacit knowledge from above-ground open and closed storage systems can help to prepare for and to prevent unwanted incidents in the underground storage. This kind of experience has been also found useful for developing the leading or early warning indicators for underground storage. Examples are given on observed autoignition and freezing of coal in the storage silos, and on occupational hazards. Selection of the leading indicators needs to consider the specific features of the unique underground facility.

Application of Bayesian networks in quantitative risk assessment of subsea blowout preventer operations

This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three-axiom-based analysis partially validates the correctness and rationality of the proposed Bayesian network model.

Application of a generic bow-tie based risk analysis framework on risk management of sea ports and offshore terminals

Ports and offshore terminals are critical infrastructure resources and play key roles in the transportation of goods and people. With more than 80 percent of international trade by volume being carried out by sea, ports and offshore terminals are vital for seaborne trade and international commerce. Furthermore in today's uncertain and complex environment there is a need to analyse the participated risk factors in order to prioritise protective measures in these critically logistics infrastructures. As a result of this study is carried out to support the risk assessment phase of the proposed Risk Management (RM) framework used for the purpose of sea ports and offshore terminals operations and management (PTOM). This has been fulfilled by integration of a generic bow-tie based risk analysis framework into the risk assessment phase as a backbone of the phase. For this reason Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) are used to analyse the risk factors associated within the PTOM. This process will eventually help the port professionals and port risk managers to investigate the identified risk factors more in detail. In order to deal with vagueness of the data Fuzzy Set Theory (FST) and possibility approach are used to overcome the disadvantages of the conventional probability based approaches.

Investigations of Human and Organizational Factors in hazardous vapor accidents

This paper presents a model to assess the contribution of Human and Organizational Factor (HOF) to accidents. The proposed model is made up of two phases. The first phase is the qualitative analysis of HOF responsible for accidents, which utilizes Human Factors Analysis and Classification System (HFACS) to seek out latent HOFs. The hierarchy of HOFs identified in the first phase provides inputs for the analysis in the second phase, which is a quantitative analysis using Bayesian Network (BN). BN enhances the ability of HFACS by allowing investigators or domain experts to measure the degree of relationships among the HOFs. In order to estimate the conditional probabilities of BN, fuzzy analytical hierarchy process and decomposition method are applied in the model. Case studies show that the model is capable of seeking out critical latent human and organizational errors and carrying out quantitative analysis of accidents. Thereafter, corresponding safety prevention measures are derived.