Accident Analysis Research Articles

Correlational analysis of occupational accidents and the safety policies in the Chinese coal mining industry from 2008 to 2021

This study investigates the correlation between previous coal mine safety policies and accidents in China. Data on coal mine accidents and government regulatory information from 2008 to 2021 are collected. The characteristics of coal mine accidents are analyzed, and safety policy indexes are identified. An ordinary least squares (OLS) regression model is established to quantitatively analyze the correlation between accidents and safety policy. The study finds that safety policies have some impact on accident occurrence in coal mines. Although there has been a decrease in accidents and deaths over time, higher mortality rates are observed during periods of increased production intensity and on weekends. Gas accidents are the most common, followed by roof and flood accidents. The study concludes that national safety policies with wider coverage and a stronger system are effective in preventing accidents, but caution should be exercised to avoid reduced vigilance with decreasing death rates.

Human factors analysis of coal mine gas accidents based on improved HFACS model

AbstractGas accidents represent a crucial domain of coal mine safety research, as they result in substantial property damage, environmental pollution, and even loss of life compared to other types of accidents. Particularly, human factors play a significant role in the majority of mining accidents. The objective of this paper is to enhance the quality of coal mine safety management, minimize the occurrence of adverse human factors in gas accidents, and analyze the factors influencing coal mine gas accidents using the Human Factors Analysis and Classification System (HFACS). To commence, this paper has devised a human factor influence index system based on the enhanced HFACS for coal mine gas accidents. Subsequently, the Decision‐making Trial and Evaluation Laboratory (DEMATEL) method has been employed to quantitatively delineate the causal relationships among these factors. Lastly, this paper utilized the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method to evaluate the importance of factors influencing coal mine gas accidents. The research findings indicate that through the utilization of the DEMATEL methodology for centrality and causal relationship calculations, the centrality and causality values associated with poor organizational management emerge as the foremost among all factors. This underscores the pivotal role that poor organizational management plays in the human factors influencing coal mine gas accidents. Furthermore, a meticulous examination using TOPSIS identified the top five indicators of influence capability: cognitive errors > habitual violations > operational management > management process > resource management. The analysis of human factors in coal mine gas accidents can provide enhanced theoretical support for the management of production safety in coal mines, as well as the prevention of gas accidents.

Highway Construction Safety Analysis Using Large Language Models

The highway construction industry carries substantial safety risks for workers, necessitating thorough accident analyses to implement effective preventive measures. Current research lacks comprehensive investigations into safety incidents, relying heavily on conventional statistical methods and overlooking valuable textual information in publicly available databases. This study leverages a state-of-the-art large language model (LLM), specifically OpenAI’s GPT-3.5 model. The primary focus is to enhance text-based incident analysis that is sourced from OSHA’s Severe Injury Reports (SIR) database. By incorporating novel natural language processing (NLP) techniques, dimensionality reduction, clustering algorithms, and LLM prompting of incident narratives, the study aims to develop an approach to the analysis of major accident causes in highway construction. The resulting cluster analysis, coupled with LLM summarization and cause identification, reveals the major accident types, such as heat-related and struck-by injuries, as well as commonalities between incidents. This research showcases the potential of artificial intelligence (AI) and LLM technology in data-driven analysis. By efficiently processing textual data and providing insightful analysis, the study fosters practical implications for safety professionals and the development of more effective accident prevention and intervention strategies within the industry.

A system theory based accident analysis model: STAMP-fuzzy DEMATEL

This paper presents a novel framework that combines the Systems-Theoretic Accident Model and Processes (STAMP) with Decision Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy logic to achieve a quantitative non-linear accident analysis methodology. The proposed methodology consists of two primary stages. Firstly, a qualitative analysis of a fatal accident is conducted using STAMP. The accident's control structure is constructed, and unsafe control actions (UCAs) are pinpointed. Secondly, the Fuzzy-DEMATEL approach is employed to quantify and determine the relationships between different control levels. A diverse pool of experts’ opinions is leveraged to assess the impact of each control level on others and accident scenarios. The findings reveal that various interactions between Operation, HSE, Business, Contracting, Firefighting, and Management levels contributed to the occurrence of the accidents. The integrated approach not only addressed qualitative nature of STAMP, but also elucidates the cause-effect relationships among control levels. The significance of this study lies in its ability to not only analyze past accidents but also proactively examine the system and identify hazardous control levels before accidents occur. By prioritizing control levels and understanding their interconnections, organizations can implement targeted safety measures to prevent accidents and enhance overall system reliability and resilience. Its effectiveness in quantifying data, identifying unsafe control actions, and establishing cause-effect relationships make it a valuable tool for enhancing safety in various complex socio-technical systems beyond the process industry.

Second Report on Italian Sea Workers: Activities and risk factors

This is a summary of the results of a research work, born from a collaboration between multiple Italian bodies and published by the Italian Workers' Compensation Authority (Inail) in March 2024: Second report on maritime workers. Activities and risk factors of sea workers.To disseminate the contents, the work offers a global overview of safety and health in the sector: with an analysis of accidents in the sector (which also involves some remarks on the event reporting form), workers' risk perception is examined and occupational diseases monitored through the MalProf system, managed by the Inail Research Sector and the local health authorities. Finally, an in-depth study is dedicated to exposure to asbestos on ships.

Risk Cause Analysis of Runway Excursion Accidents in the Aircraft Landing Stage through Integrated FTA-BN Method

Risk Cause Analysis of Runway Excursion Accidents in the Aircraft Landing Stage through Integrated FTA-BN Method

Analysis of the Risk of Work Accidents for Field Officers PT PLN West Tanjung Jabung Regency, Jambi Province using the Hiradc Method

The aim of this research is to analyze the risk of work accidents among PT field officers. PLN West Tanjung Jabung Regency, Jambi Province using the HIRADC method. This type of research is based on the philosophy of post-positivism and is carried out to study the state of natural objects (not experiments). There were 7 research informants including 1 key informant, namely the K3L Implementing Officer of PT. PLN Kuala Tungkal, 2 main informants, namely the Head of PT. PLN Kuala Tungkal, and PT Performance Analysis. PLN Kuala Tungkal, 4 supporting informants, namely PT field employees. PLN Kuala Tungkal. Based on the results of the research, conclusions related to the risk analysis of work accidents among PT field officers. PLN West Tanjung Jabung Regency found 11 potential dangers after evaluating the risk ranking of accidents in the implementation of electricity production work in the operations division. There are 15 types of risk (37.5%) classified as medium risk, 22 types of risk (55%) classified as high risk, and 3 types of risk (7.5%) classified as very high risk (extreme risk). , and After analyzing the classification of work procedures, there are several work procedures, namely inappropriate work procedures, not being disciplined in using PPE when working.

Spatial-Temporal Analysis of Road Accidents in Haraz Road Using Spatial Statistics and Geographical Information System

Spatial-Temporal Analysis of Road Accidents in Haraz Road Using Spatial Statistics and Geographical Information System

The RADTRAD analysis methodology for the Fuel Handling Accident during the long-term shutdown period of Chinshan Nuclear Power Plant

Abstract This study presents a methodology for the Fuel Handling Accident (FHA) analysis during the long-term shutdown period with the fuels still residing in the reactor core for the Chinshan BWR/4 Nuclear Power Plant. This methodology can be used to calculate the radiation dose received by the control room personnel, assuming failure in safety systems, facilities or structures. The computer code RADTRAD is employed to perform the transport and release of radioactive inventories first, and then calculate the control room dose based on appropriate dose conversion factor for the FHA. The results show that even failure in safety systems, facilities or structures, the whole body dose, thyroid dose and beta-skin dose are well below the acceptance limits specified in the Standard Review Plan.

Analysis of Traffic Accidents Based on the Integration Model

To enhance the safety of road traffic operations, this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics. Initially, the process involved data cleaning, transformation, and normalization. Subsequently, various classification models were constructed, including logistic regression, k-nearest neighbors, gradient boosting, decision trees, AdaBoost, and extra trees models. Evaluation metrics such as accuracy, precision, recall, F1 score, and Hamming loss were employed. Upon analysis, the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models. Based on the model’s output results, an in-depth examination of the factors influencing traffic accidents was conducted. Additionally, measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented. These findings served as a valuable reference for mitigating the occurrence of traffic accidents.

Analysis of Accidents in Petroleum and Chemical Industries

This study determined the critical risk factors that influence the risk of major accidents in petroleum and chemical industries. The retrospective study assessed five iconic accidents using the tripod beta analysis methodology. These failures were aggregated into critical risk factors for these major accidents. The findings of this study revealed empirically that there are broadly five critical risk factors that influence the risk of major accidents: (i) design flaws, (ii) site process safety management, (iii) barrier management / control, (iv) operating procedures, and (v) process safety culture. These findings highlight that major accident risks can accumulate “dangerous” from the impairment of safety-critical barriers and that the operators of petroleum and chemical facilities may be blind-sided to the cumulative risk of the impairments. The findings also highlight the need for a proactive risk assessment tool with the capability to check the health of safety-critical barriers on a real time basis, in an operating plant. The use of Tripod Beta as a tool in accident causation analysis is recommended

Safety Risk Analysis Optimization Using Fuzzy: A Literature Review

Risk analysis is the procedure to identify risks to the surrounding environment, especially in the workplace. There are several methods for analyzing risk. Nowadays, many risk analysis methods are integrated with optimization methods, such as fuzzy. This approach is used to optimize the results of risk analysis. This study aims to investigate the development of collaboration between risk analysis methods and fuzzy in the industrial sector and research that can be carried out in the future. The authors collected 500 articles from Dimensions.ai between 2018 and 2023 and evaluated the references obtained with the bibliometric analysis tool. The authors analyzed the dataset obtained based on the countries that conducted the most research on safety risk analysis optimization and collaboration, themes that were widely used, the themes distribution, and the relationship between existing themes. The Results show that China has published many studies in collaboration with other countries. Research themes that use risk analysis methods are widely used in human assessment where it can be seen in the keywords. Currently, there has been a lot of research on risk assessment integrated with fuzzy and humans. For future research, fuzzy approaches and risk analysis can be integrated with other accident analysis methods. Keywords: Bibliometric, Fuzzy, Literature Review, Risk Analysis, Risk Assessment

Coupling analysis of crane accident risks based on Bayesian network and the N-K model

Crane usage is pervasive on construction sites, however, it is associated with a notably high accident rate. The analyzing of crane accident risks is essential for accident prevention, control, and ensuring the safety of lifting operations. Hence, significant emphasis should be placed on understanding the interaction among various risk factors. This paper proposes a quantitative coupling method for human, machine, management, and environmental risk factors in crane accidents, leveraging Bayesian networks (BN) and the N-K model. Firstly, text mining technology and fault tree analysis are employed to analyze the causes of crane accidents and categorize the associate risk factors. Secondly, the types of risk coupling resulting from human, machine, management, and environmental risk factors are defined. Thirdly, the BN model is developed based on the analysis of crane accident risksand its N-K model. Fourthly, the parameters of the risk coupling nodes in the developed BN are determined based on the calculation results of the N-K model. Finally, for the risk coupling types with high coupling values and the first-level node and second-level node, the failure probability is analyzed through posterior probability and sensitivity analysis. The results indicate that factors related to man and management significantly impact crane accidents and warrant enhanced attention. The interplay among multiple risk factors significantly influences the probability of crane accidents, necessitating careful attention.

Predictive analysis for road accidents using a tree-based and deep learning fusion system

While road accidents’ prediction has been of crucial importance in the development of intelligent transportation technologies; a profound analysis within the driver-vehicle-environment system is no doubt of great interest and necessity. Three categories of features namely vehicle kinematics, driver inputs and environmental conditions collected using a desktop driving simulator have been systematically recorded in order to outline a fusion strategy based on various base classifiers and a Meta classifier that learns from base classifiers’ results to acquire more efficient accidents’ predictions. Highly heuristic optimized tree-based models namely AdaBoost, XGBoost, RF along with the MLP deep learning technique have been endorsed to establish effective predictions. Furthermore, to ensure that the proposed system provide superior and stable decisions as road accidents are generally unexpected and occur rarely, an imbalance-learning approach was conducted to add to the current knowledge by adopting three performant balancing strategies: ROS, SMOTE and ADASYN. To the best our knowledge, there has been a limited interest at adopting a fusion-based system examining the impact of real-time features’ combinations and fused tree-based models along with deep learning technique as meta-classifier on the prediction of road accidents while taking into account class imbalance. The findings depict that the superior performance of the proposed fusion system with precision, recall and f1-score over 90%. As a whole, the results highlight the significance of the explanatory features related to potential accidents and can be employed in designing efficient intelligent transportation systems.

Cause analysis of secondary explosion accident in Hushan Gold Mine, Shandong Province, China based on HFACS-CM model

Explosion accidents often occur during mining, especially sudden secondary explosions, which pose a serious threat to the life and property safety of employees. Based on the development process and consequences of typical secondary explosion accidents in Hushan Gold Mine, Qixia City, Shandong Province, China, this paper uses accident analysis model and cone measurement calculation method to determine the direct cause of secondary explosion accidents. The main conclusions are as follows: The HFACS-CM model is used to analyze the causes of secondary explosion from five aspects: external environment, organizational influence, unsafe leadership, the prerequisite conditions of unsafe behavior and unsafe behavior. It is found that the CO ignition explosion in the mining process is the direct cause of secondary explosion. Then, combined with the experimental data of the cone measuring instrument, the field CO amount was calculated, and the MCO ≥ 101.4 m3 was obtained, indicating that the CO production did reach the condition of explosion range, and the analysis results of the model were verified. Through HFACS-CM model analysis and calculation verification, the key causes of secondary explosion accidents in such mines are found and lessons are learned, which provides an effective reference for the effective prevention of deflagration accidents in the field of mine safety.

A chemical accident cause text mining method based on improved accident triangle

BackgroundWith the rapid development of China’s chemical industry, although researchers have developed many methods in the field of chemical safety, the situation of chemical safety in China is still not optimistic. How to prevent accidents has always been the focus of scholars’ attention.MethodsBased on the characteristics of chemical enterprises and the Heinrich accident triangle, this paper developed the organizational-level accident triangle, which divides accidents into group-level, unit-level, and workshop-level accidents. Based on 484 accident records of a large chemical enterprise in China, the Spearman correlation coefficient was used to analyze the rationality of accident classification and the occurrence rules of accidents at different levels. In addition, this paper used TF-IDF and K-means algorithms to extract keywords and perform text clustering analysis for accidents at different levels based on accident classification. The risk factors of each accident cluster were further analyzed, and improvement measures were proposed for the sample enterprises.ResultsThe results show that reducing unit-level accidents can prevent group-level accidents. The accidents of the sample enterprises are mainly personal injury accidents, production accidents, environmental pollution accidents, and quality accidents. The leading causes of personal injury accidents are employees’ unsafe behaviors, such as poor safety awareness, non-standard operation, illegal operation, untimely communication, etc. The leading causes of production accidents, environmental pollution accidents, and quality accidents include the unsafe state of materials, such as equipment damage, pipeline leakage, short-circuiting, excessive fluctuation of process parameters, etc.ConclusionCompared with the traditional accident classification method, the accident triangle proposed in this paper based on the organizational level dramatically reduces the differences between accidents, helps enterprises quickly identify risk factors, and prevents accidents. This method can effectively prevent accidents and provide helpful guidance for the safety management of chemical enterprises.

Construction of Event Graph for Ship Collision Accident Analysis to Improve Maritime Traffic Safety

At present, there are three main methods for analyzing the causes of ship collision accidents: statistical analysis, accident causation models, and knowledge graphs. With the deepening of research, the analysis methods pay more attention to the objective correlation between various factors of the accident, and the analysis results obtained are more objective and accurate. On this basis, this paper proposes a method for analyzing the contribution degree of different causes and accident conduction paths in ship collision accidents based on the construction of the Ship Collision Accidents Event Graph (SCAEG). Firstly, the ontology is constructed based on the grounded theory. Secondly, events and relationships are extracted after fine‐tuning the UIE model. Thirdly, the SCAEG is constructed after event coreference resolution. Finally, this research conducts the contribution degree analysis, accident conduction path analysis, and accident spatial distribution analysis based on SCAEG. The advantages of this method include the following: (i) it can construct a more complete and accurate ontology; (ii) adopting this approach can unify various information extraction tasks and achieve good results based on small sample annotation data; and (iii) using this method, we can conduct contribution degree analysis of different causes, accident conduction path analysis, and spatial distribution analysis. Experimental evidence demonstrates the effectiveness of this method. The analytical results obtained from the experiments can provide assistant decision‐making for relevant departments to reduce the occurrence of ship collision accidents and improve maritime traffic safety.

Collision and Contact – Analysis of Accidents at Sea

Collision and Contact – Analysis of Accidents at Sea

Prevention, social emancipation, and paradigmatic transition: a 40-year interdisciplinary Brazilian trajectory on accidents and disasters.

The article, in the form of an essay, systematizes a 40-year-long professional trajectory of interdisciplinary and socially engaged experiences around the analysis and prevention of accidents and disasters. This study was mainly developed within the scope of research and postgraduate studies in Public Health in Brazil, driven by the sanitarian movement and the construction of Brazilian Unified National Health System (SUS) in its search for democracy and social and health justices. Its empirical basis involved workers' health and environmental surveillance actions organized in networks led by SUS in conjunction with universities, unions, social movements, environmental nongovernmental organizations (NGO), and Public Prosecutors' Offices. Events of greater socio-environmental complexity in sectors such as steel, petrochemicals, mining, agribusiness, and energy forged the search for new epistemic and interdisciplinary references that encompassed two new justices, i.e., environmental and cognitive. This essay systematizes this trajectory of conceptual contributions in three movements from the 1980s to the present day (each corresponding to a socio-political and institutional context) to reflect on paradigmatic transition movements in the analysis and prevention of accidents and disasters from an interdisciplinary perspective. It ends by suggesting abyssal and emancipatory prevention to face different current crises, including environmental, health, democratic, and civilizing ones.

Prevenção, emancipação social e transição paradigmática: uma trajetória interdisciplinar de 40 anos no Brasil sobre acidentes e desastres

Abstract: The article, in the form of an essay, systematizes a 40-year-long professional trajectory of interdisciplinary and socially engaged experiences around the analysis and prevention of accidents and disasters. This study was mainly developed within the scope of research and postgraduate studies in Public Health in Brazil, driven by the sanitarian movement and the construction of Brazilian Unified National Health System (SUS) in its search for democracy and social and health justices. Its empirical basis involved workers’ health and environmental surveillance actions organized in networks led by SUS in conjunction with universities, unions, social movements, environmental nongovernmental organizations (NGO), and Public Prosecutors’ Offices. Events of greater socio-environmental complexity in sectors such as steel, petrochemicals, mining, agribusiness, and energy forged the search for new epistemic and interdisciplinary references that encompassed two new justices, i.e., environmental and cognitive. This essay systematizes this trajectory of conceptual contributions in three movements from the 1980s to the present day (each corresponding to a socio-political and institutional context) to reflect on paradigmatic transition movements in the analysis and prevention of accidents and disasters from an interdisciplinary perspective. It ends by suggesting abyssal and emancipatory prevention to face different current crises, including environmental, health, democratic, and civilizing ones.