Accident Investigation Reports Research Articles

Complex network-based risk analysis for maritime heavy casualties in China during 2012–2021

A collection of accident investigation reports for the 21 maritime heavy casualties that occurred in China during 2012–2021 is studied statistically and analytically in the present study. The basic accident factors are first extracted from these investigation reports to present the distribution features of these heavy casualties. Then, the accident factors in terms of crew, environment, ship equipment and management in each individual investigation report are logically linked to develop a causative chain. As a result, 21 chains developed from the 21 accident investigation reports are interweaved into a complex network by sharing the same factors. The accident factors involved in the complex network are quantitatively assessed in terms of activeness, connection strength and criticality based on the network topology parameters and the improved K-shell decomposition algorithm. The topology of the developed complex network implies that the occurrence of maritime heavy casualties is under control, and the results show that the seaworthiness of crew, ship equipment and cargo is critical to maintain safe operation on board; meanwhile, supervising ship operators and managers to ensure that they fulfil their responsibility is an effective way to maintain the maritime safety level.

Firefighting Water Tanker Commuting Accident: Case report on why it happened and how to manage

Road traffic accidents represent a significant global public health concern, and Malaysia is no exception. Within the Fire and Rescue Department of Malaysia (FRDM), similar issues have had detrimental impacts on health, financial loss, and service disruptions from 2016 to 2021. These challenges were highlighted by a recent accident involving a water tanker during a road test, described in this study according to qualitative data from the FRDM accident investigation report. The investigation revealed that human factor, as the active failure, was the primary cause of the accident. Response errors stem from a cascade of latent conditions initiated by inadequate resource management (i.e. provision of training and budget allocation). To address these challenges, the FRDM should adopt a comprehensive approach to address both latent conditions and active failures. This comprehensive approach ensures that both immediate concerns and root causes are addressed, leading to sustained improvements in safety and operational performance.

Framework for process risk analysis of maritime accidents based on resilience theory: A case study of grounding accidents in Arctic waters

The complexity and non-linear interactions involved in maritime accidents make it challenging to apply traditional models of risk analysis to them. Resilience is adopting a perspective focuses on holistic aspects of safety across the pre-accident, mid-accident, and post-accident phases. This paper proposes a framework for process risk analysis of maritime accidents based on resilience theory by applying the function resonance analysis method (FRAM). We first identify a three-layer maritime accident based on maritime accident investigation reports and then analyze the potential variability of all relevant functions. Subsequently, a FRAM model is constructed to analyze the accident's functional resonance of accidents by aggregating variations in the functions. Finally, we apply Monte Carlo simulations to compute the values of coupling of all interconnected functions. The ship grounding accidents in Arctic waters is chosen as the case to demonstrate the proposed framework. The relationships of influence between the functions were analyzed to obtain critical coupling pathways of coupling between them, and a time-weighted PageRank algorithm was used to identify key functions. The results show that the proposed approach can reveal vulnerabilities and instabilities in the system, and enables the proposal of targeted, long-term measures of risk control to enhance its resilience and safety.

COLREGs Misunderstood or Ignored?

As various scholarly articles and accident investigation reports indicate, the risk of collisions at sea is still high. The objective of this research is to identify if this is due to misunderstanding of the International Regulations for Preventing Collisions at Sea 1972, as amended (COLREGs), by watchkeeping officers working onboard cargo ships. A total of 76 collisions while navigating officers (OOW) were at the con that occurred during a period of 10 years were analysed.80% of the collisions could have been avoided since one vessel was aware of the presence of the other. The highest number of collisions had taken place in cases of crossing situations. Furthermore, it is also important to note that 52% of the OOWs involved in collisions were sufficiently experienced.No evidence was found to conclude that these accidents occurred due to misinterpretation or misunderstanding of the COLREG’s by the OOW’s onboard the cargo ships, but as incorrect or insufficient action had been taken by experienced OOW’s, this could have been due to complacency and/or overconfidence on their part.Among the measures to be taken to minimize collisions at sea, overconfidence and complacency are two factors that should also require attention by all interested parties.

Evolutionary model and risk analysis of ship collision accidents based on complex networks and DEMATEL

To manage and control ship collision risk, this paper aims to develop a quantitative risk analysis approach based on complex networks and Decision-making Trial and Evaluation Laboratory (DEMATEL), to study the evolution of ship collision accidents. Firstly, through the search, selection and analysis of accident investigation reports of ship collisions in the world, 46 Risk Influential Factors (RIFs) were identified, and a network model for the evolution of ship collision was established. Secondly, a risk analysis of the evolution model for ship collision accidents was conducted by utilizing the topology characteristics of the complex network theory. Thirdly, a robustness analysis was used to identify 10 key RIFs involved in ship collisions from a global network perspective, and the grey relation analysis technique was employed to verify the key RIFs. Finally, the DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality, and corresponding risk control measures were proposed to mitigate those key RIFs. The research results provide theoretical support for identifying the key RIFs and controlling the risk transmission of ship collision, and have practical significance for preventing the occurrence of ship collision accidents and ensuring the safe navigation of ships.

Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention.

Cause Analysis of Elevator Accidents Based on Complex Network

In order to excavate the key causes of elevator accidents, 70 elevator accident investigation reports from 2018 to 2023 were collected, and data mining was carried out to extract "4M" unsafe factors. On this basis, based on the complex network, the model of elevator accident causation network is constructed, and the statistical characteristics of this model are analyzed. Finally, the entropy weight-TOPSIS is used to construct a comprehensive evaluation model of network node importance, which comprehensively evaluates the causes of elevator accidents from four indicators, including degree centrality, intermediary centrality, proximity centrality and aggregation coefficient of nodes, and identifies the key factors of elevator accidents. The results show that unsafe factors such as defects of equipment and facilities, illegal operation, improper operation, weak safety awareness, inadequate implementation of safety responsibilities, and inadequate on-site supervision are the key causes of elevator accidents, which need to be controlled and controlled.

A socio-technical systems approach of the accident analysis in Indonesian multiple train accident cases: An application of AcciMap methodology.

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.

Exploring human factors of major chemical accidents in China: Evidence from 160 accidents during 2011–2022

Major chemical accidents (MCAs) usually cause multiple casualties, property losses, and environmental damage. MCAs in hazardous chemical production enterprises (HCPEs) in China have been frequent in recent years. Understanding past accidents is essential for accident prevention and risk mitigation. However, no accurate statistical analysis and systematic human cause investigation of MCAs in China is available. Hence, this study systematically identified the main characteristics and human factors of MCAs in China during 2011–2022 using official accident investigation reports. First, the 160 MCAs were investigated by mathematical and statistical methods involving eight main characteristics: evolution trend, spatial locations, accident types, domino effects involved, occurrence links, time volatility, hazardous chemicals involved, and enterprise scales. Second, based on the characteristics of MCAs and existing research on chemical accidents, the Human Factors Analysis and Classification System (HFACS) for Chemical Accidents (HFACS-CA), which consists of 26 cause categories and is suitable for the analysis of chemical accidents, was proposed on the basis of HFACS and other extended HFACS. Then, the selected 146 representative MCAs were analyzed using the HFACS-CA framework; the human factors (comprising human and organizational factors (HOFs) and outside factors) of MCAs in China were systematically explored. Finally, according to the main characteristics and human factors of MCAs, comprehensive recommendations for the prevention of MCAs involving enterprises, governments, and interested parties were proposed. This work can provide a useful human factor analysis tool for investigating chemical accidents and valuable information for preventing MCAs in China and other countries.

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

AbstractCurrently, the digital transformation of the power grid is underway, and the intelligent health management technology for power transformers is rapidly advancing. However, there are issues in the operation and maintenance process, such as weak information correlation and low decision‐making efficiency. Knowledge graphs have been applied in other industrial fields, such as spacecraft maintenance, to significantly improve knowledge query efficiency. However, there is a lack of literature on knowledge graph construction in the field of power transformer operation and maintenance. Additionally, there is limited publicly available data and difficulties in effectively mining operation and maintenance knowledge in this field. A method for constructing a knowledge graph for power transformer operation and maintenance based on ALBERT is proposed. Firstly, publicly available literature in the field of power transformers is collected, and a sample enhancement method using regular matching is used to enrich the semi‐structured corpora, such as power system accident investigation reports, to construct a training dataset for power transformer operation and maintenance. Then, the ALBERT‐BiLSTM‐CRF deep learning algorithm is applied to extract power transformer operation and maintenance entities from the relevant literature and accident investigation reports, and this method is compared with traditional deep learning algorithms to demonstrate its superiority. Subsequently, the ALBERT‐BiLSTM‐Attention deep learning algorithm, which incorporates ALBERT and attention mechanism, is utilised to extract relationships between power transformer operation and maintenance entities. Compared to other deep learning algorithms, this algorithm demonstrates better performance in the domain‐specific texts of power transformer operation and maintenance. Finally, the Neo4j graph database is used to visualise and present the knowledge graph, enabling decision support based on the power transformer operation and maintenance knowledge graph.

How design decisions change fall from heights in reinforced concrete structures?

Prevention through Design (PtD) and Design for Safety (DfS) are now being considered in relation to occupational health and safety and building design. A recent collection of studies suggests that certain decisions made by designers during the design process are at the root of many risks. A study was undertaken to relate falls from height to the conventional design process for reinforced concrete buildings. This was achieved by dividing the building design process into stages. During the design phase, input was gathered from architects and engineers working in the sector. Accident types were identified based on studies of occupational accident investigation reports from the Ministry of Labor. Of the 15 types of fall from height accidents studied, 12 were attributed to design decisions. Of these, 6 different accident types were associated with 5 or more design decisions, while 2 of these were associated with 3 or 4 design decisions. The remaining 4 types were linked to only 1 design decision.

Automatic Identification of Causal Factors from Fall-Related Accident Investigation Reports Using Machine Learning and Ensemble Learning Approaches

Automatic Identification of Causal Factors from Fall-Related Accident Investigation Reports Using Machine Learning and Ensemble Learning Approaches

Mining the Critical Factors of Hazardous Material Road Transportation Accidents From Chinese Investigation Reports

Rather than reducing losses after an accident, identifying critical accident factors is a better approach because it can be used to prevent accidents. Hazard materials (HAZMAT) road transportation is a complex sociotechnical system that involves three major procedures. Staff, vehicles, and tanks directly participate in this transport system, while different companies and government departments manage or supervise personnel and equipment. Moreover, all these stakeholders must obey ten types of industrial standards or regulations. The accident investigation reports contain the critical factors of the accident. Specifically, using a series of natural language processing (NLP) technologies, the accident causes in 137 Chinese reports are extracted, classified, and formatted. Then, a Bayesian network (BN) is constructed using the structural restrictions learning algorithm based on structured accident data from the reports. Finally, critical accident factors are revealed through sensitivity analysis of the BN. The results show that inadequate or delayed operation, which includes subordinate management factors and supervision factors, is the most critical factor among the five factors grouped. Actual accident data can explain the results. Combining the actual data and accident model, the accident analysis approach proposed in this paper considers the structural relations and frequency of accident factors.

Identification of Risk Influential Factors for Fishing Vessel Accidents Using Claims Data from Fishery Mutual Insurance Association

This research aims to identify and analyze the significant risk factors contributing to accidents involving fishing vessels, a crucial step towards enhancing safety and promoting sustainable practices in the fishing industry. Using a data-driven Bayesian network (BN) model that incorporates feature selection through the random forest (RF) method, we explore these key factors and their interconnected relationships. A review of past academic studies and accident investigation reports from the Fishery Mutual Insurance Association (FMIA) revealed 17 such factors. We then used the random forest model to rank these factors by importance, selecting 11 critical ones to build the Bayesian network model. The data-driven Bayesian network (BN) model is further utilized to delve deeper into the central factors influencing fishing vessel accidents. Upon validation, the study results show that incorporating the random forest feature selection method enhances the simplicity, reliability, and precision of the BN model. This finding is supported by a thorough performance evaluation and scenario analysis.

Environmental Factors of Commuting Accidents among Firefighter Drivers in Peninsular Malaysia: A Case study

Introduction: Commuting accidents (CAs) caused significant detrimental impacts on the Malaysian fire department. Therefore, this study aims to identify factors of CA by mainly focusing on environmental factors. Methodology: The qualitative data was obtained from accident investigation reports from five states (Kedah, Selangor, Kuala Lumpur, Negeri Sembilan and Johor) between 2017-2021. The data were analysed using content analysis. Results: The identified environmental factors categories were 1) environmental conditions, 2) time, 3) road conditions and 4) road environment. Conclusion: These findings provide valuable information on environmental factors that contributed to accidents among firefighters, which could be useful for future research and intervention plans. Keywords: Commuting accident – environmental factor - firefighter

Towards system-theoretic risk management for maritime transportation systems: A case study of the yangtze river estuary

This paper presents a novel framework for system-theoretic risk management of maritime transportation systems (MTS) based on the principles of formal safety assessment. The proposed framework comprises four essential steps: (1) risk identification: identifying accident scenarios and associated risk-influencing factors in the MTS through text mining of maritime accident investigation reports; (2) risk analysis: determining the probabilities and consequences of accident scenarios in the MTS using Bayesian network modeling; (3) risk evaluation: assessing the risks of various accident scenarios and the comprehensive risk of the MTS, and comparing the results with risk acceptance criteria to make informed decisions; and (4) risk treatment: proposing effective risk control options (RCOs) for accident scenarios and the comprehensive risk of the MTS by incorporating expert judgment, fuzzy sets, and Bayesian network modeling. To demonstrate the applicability of the proposed framework, the risk management of the Yangtze River Estuary is presented as a case study. By adopting a comprehensive approach to risk management, stakeholders gain a more holistic view of risk and achieve more effective decision-making processes for RCOs. The framework may help improve the safety and security of maritime operations, enhance the efficiency and competitiveness of the industry.

Cause Analysis of Coal Mine Gas Accidents in China Based on Association Rules

Coal mine gas accidents will cause great economic losses and casualties. It is of great significance to find out the essential causes of coal mine gas accidents and put forward measures to prevent them. In this paper, 110 coal mine gas accidents which occurred in China from 2001 to 2022 are selected to analyze the causes of the accidents by extracting the keywords of human factors, equipment factors, environment factors, and management factors from the accident investigation reports. Firstly, the accident statistical analysis is carried out from three dimensions of factor frequency, accident type, and accident grade. Secondly, the Apriori algorithm is used for data mining to obtain frequent item sets and association rules of coal mine gas accident factors. Finally, the coal mine gas accident cause chains which are obtained by using the association rule. The frequent terms of 9 factors, 23 association rules, and 3 coal mine gas accident cause chains are obtained. The results show that the production of coal mine enterprises by illegal organizations is an important reason for the occurrence of coal mine gas accidents. The lack of good management culture easily leads to habitual violations of personnel and decision-making errors, and then causes equipment problems, reflected in the ventilation system which is not perfect, resulting in gas accumulation. The occurrence of coal mine gas accidents can be prevented to a large extent by preventing the absence of good management culture in enterprise management and the occurrence of illegal production behaviors.

Critical Analysis of Container Accidents in International Logistics

Purpose: Container accidents are a significant issue in international logistics, causing property damage, financial loss, environmental hazards, and even loss of life. These accidents can occur in various settings, such as ports, container yards, roads, rails, or seas. Efforts are being made to enhance container safety, but they remain a persistent challenge that requires ongoing attention to ensure the safe and efficient movement of goods across the globe. By understanding the specific aspects of container accidents that are human-controllable and addressing these issues, a more sustainable and efficient global logistics industry can be achieved. Methodology: An exploratory research design is used in the research study. A comprehensive review of existing literature, including research papers, academic journals, books, reports, and reputable online sources was done. Literature that focuses on container accidents, maritime safety, human factors, and accident investigation reports was extensively studied to gain an understanding of the current state of knowledge on the topic. Data from relevant secondary sources that provide information on container accidents that are human-controllable were identified and collected. Results & Outcome: Container accidents that are happening owing to natural calamities such as floods, cyclones, sea perils, etc are uncontrollable. There are other forms of container accidents viz cargo damage, container equipment damage, container vessel damage, loss of life owing to container fall, etc occurring due to container sliding while handling, tipping of container vessels while loading, skewing of container vessels in the transit voyage, etc. that are controllable by human interference. Unevenly loaded containers seem to be the prime reason for many container accidents that are apparently controllable by humans by following the International Convention for Safe Containers (CSC) guidelines of the International Maritime Organization. In an LCL (Less than Container Load) scenario, adhering to such guidelines may be challenging. There are many research articles discussing the load balancing of containers in an LCL scenario using various optimization techniques, and it appears that the literature is still evolving. Originality: A exploratory study to understand the human controllable factors in container accidents, further study the regulatory guidelines, and analyse the research papers published. Type of the Paper: Exploratory research.

Integrating Information Entropy and Latent Dirichlet Allocation Models for Analysis of Safety Accidents in the Construction Industry

Construction accident investigation reports contain critical information, but extracting useful insights from the voluminous Chinese text is challenging. Traditional methods rely on expert judgment, which leads to time-consuming and potentially inaccurate results. To overcome this problem, we propose a novel approach that combines text mining techniques and latent Dirichlet allocation (LDA) models to analyze standardized accident investigation reports in the Chinese construction industry. The proposed method integrates an information entropy term frequency-inverse document frequency (TF-IDF) weighting scheme to evaluate term importance and accounts for word and model uncertainty. The method was applied to a set of construction industry accident reports to identify the key factors leading to safety accidents. The results show that the causal factors of accidents in Chinese accident investigation reports consist of keywords and negative expressions, including “failure to timely identify safety hazards” and “inadequate site safety management”. Failure to timely identify safety hazards is the most common factor in accident investigation reports, and the negative expressions commonly used in the reports include “not timely” and “not in place”. The information entropy TF-IDF method is superior to traditional methods in terms of accuracy and efficiency, and the LDA model that considers word frequency and feature weights is better able to capture the underlying themes in the Chinese corpus. And the subject terms that make up the themes contain more information about the causes of accidents. This approach helps site managers more quickly and effectively understand the causal factors and key messages that lead to accidents from incident reports. It gives site managers insight into common patterns and themes associated with safety incidents, such as unsafe practices, hazardous work environments, and non-compliance with safety regulations. This enables them to make informed decisions to improve safety management practices.

Comprehensive Evaluation of Marine Ship Fires Risk Based on Fuzzy Broad Learning System

Ship fires exhibit the main characteristics of a high possibility of occurrence, large load, fast spreading, high difficulty in extinguishing, and serious losses. Therefore, once a fire occurs, it will cause huge loss in terms of economic and personnel safety. Firstly, a ship fire risk evaluation indicator system was constructed based on the causes and severity of the fires. Secondly, a comprehensive evaluation method for the fuzzy broad learning system (FBLS) was proposed. The fuzzy system was used to implement feature mapping on the input data, and the extracted fuzzy features were further input into the BLS enhancement layer. A fuzzy broad learning neural network structure was constructed by combining fuzzy features, feature nodes, and enhancement nodes. The method was applied to the field of risk assessment for the first time, and is a reference for subsequent studies. Finally, the risk levels of ship fires were classified and compared with evaluation methods such as fuzzy support vector machine (FSVM) and Fuzzy BP neural network (FBPNN) to demonstrate effectiveness and accuracy. The proposed FBLS method was used to predict actual cases, and the results showed consistency with the level determined by the accident investigation report published by the Maritime Bureau Administration.