Accident Evolution Research Articles

Research on scenario extrapolation and emergency decision-making for fire and explosion accidents at university laboratories based on BN-CBR

To solve the problems of suddenness, uncertainty and untimely emergency decision-making related to fire and explosion accidents in university laboratories, a combined method of BN and CBR is introduced to analyze laboratory accidents. By summarizing the characteristics of 72 accident cases worldwide, four scenario elements with key roles are extracted by combining the public safety triangle theoretical model; a BN is established from the macro perspective, which is based on the construction of dynamic scenarios; the evolution path is analyzed via BN theory; and the probability of occurrence of accidents is quantified from the microscopic perspective, with a focus on the analysis of the accidental evolution process. A case similarity calculation is carried out via CBR, and the construction of a BN-CBR-assisted decision-making model is completed, verified and corrected in an case study. The results show that the BN-CBR model can quickly determine the accident evolution path and the most similar historical cases, and its quantitative probability calculation enables one to comprehensively grasp the real-time state of the whole accident and the emergency response in a timely manner, which provides a new way to approach emergency decision-making of accidents.

Diagnosis of human envenoming by terrestrial venomous animals: Routine, advances, and perspectives

Despite the development of new and advanced diagnostic approaches, monitoring the clinical evolution of accidents caused by venomous animals is still a challenge for science. In this review, we present the state of the art of laboratory tests that are routinely used for the diagnosis and monitoring of envenomings by venomous animals, as well as the use of new tools for more accurate and specific diagnoses. While a comprehensive range of tools is outlined, comprising hematological, biochemical, immunoassays, and diagnostic imaging tools, it is important to acknowledge their limitations in predicting the onset of clinical complications, since they provide an overview of organic damage after its development. Thus, the need for discovery, validation, and use of biomarkers that have greater predictive power, sensitivity and specificity is evident. This will help in the diagnosis, monitoring, and treatment of patients envenomated by venomous animals, consequently reducing the global burden of morbidity and mortality.

Risk analysis of accident-causing evolution in chemical laboratory based on complex network

Chemical laboratories usually have many high risks from various dangerous chemicals, dangerous devices, and experimental operators involved in the laboratory. Once a severe accident occurs, the accident consequence will significantly impact scientific research innovation and social progress. Aiming at laboratory safety problems, an accident evolution model is established to reveal the critical accident-causing factors and the most likely evolutionary path of accidents in the process of accident evolution. Firstly, the 24 model is used to identify the cause factors of laboratory accidents and the causal relationship between factors. Then, the complex network model of laboratory accident evolution is constructed with accident-causing factors as nodes and causal relationships between factors as edges. Secondly, using the TOPSIS method, four typical complex network characteristic indexes, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, are constructed into a comprehensive evaluation index to reveal the key influencing factors in the accident evolution process. Finally, the fuzzy number is introduced to quantify the uncertainty of the accident evolution path. The most likely evolutionary path is obtained through the Dijkstra and risk entropy theory. Based on the data of 151 laboratory accidents from 2001 to 2022, the accident evolution analysis of chemical laboratories was carried out. The evolution results show that the maximum probability value is the evolution path of fire accidents, and the minimum probability value is the evolution path of electric shock accidents, which accords with the general statistical law of accidents. When applied to the actual laboratory, the safety management level of the laboratory can be effectively improved by controlling the key influencing factors and cutting off the accident evolution path.

Risk causation analysis and prevention strategy of working fluid systems based on accident data and complex network theory

Working fluid systems (WFS) in industrial processes are often accompanied by hazardous gases, which lead to various hazardous accidents. This paper, based on accident data and complex network theory, develops a framework for risk causation analysis and prevention strategies for WFS. The accident components at WFS are identified with emphasis. In the case study, a set of risk event nodes, including system, equipment, personnel, environment, and accident types, are selected based on the characteristics of systems. A complex network model according to accident chains is constructed to represent the complex interactions and relationships between various factors. This model evaluates the structural characteristics of the network and elucidates the relationship between accident causes and system risks. The comprehensive importance of different nodes is analyzed in order to rank the risk factors and determine three types of critical causative paths for the risks of the accident chain. This method has been verified to help identify critical factors in accidents when combined with robustness analysis. The risk prevention strategies are proposed to reduce the risk of accident evolution. The method can help identify the accident risk characteristics of WFS, providing valuable information for safe application and accident prevention.

Dynamic scenario deduction analysis for hazardous chemical accident based on CNN‐LSTM model with attention mechanism

AbstractThe evolution of hazardous chemical accidents (HCAs) is characterized by uncertainty and complexity. It is challenging for decision‐makers to expeditiously adapt emergency response plans in response to dynamically changing scenario states. This study proposes a data‐driven methodology for constructing accident scenarios and develops a novel hybrid deep learning model for scenario deduction analysis. This model aids in accurately predicting the evolution of HCAs, enabling emergency responders to prepare and implement targeted interventions proactively. First, a framework for constructing an accident scenario database is presented, based on the time‐sequential characteristics of accident progression. This framework employs a data‐driven approach to describe the evolution process of accident scenarios. Second, a deep learning model (CNN‐LSTM‐Attention) that integrates convolutional neural network (CNN), long short‐term memory (LSTM), and attention mechanism (AM) is developed for accident scenario deduction analysis. Finally, to illustrate practical application, a scenario database for HCAs is established. A major HCA case study is conducted to demonstrate the ability of this model to analyze various scenarios, thereby improving emergency decision‐making efficiency. Compared with algorithms such as CNN, LSTM, and CNN‐LSTM, the prediction accuracy of this method ranges from 86% to 93%, signifying an improvement of over 7%. This work provides a reliable framework for supporting decision‐making in emergency management.

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.

Identifying spatio-temporal pattern of electric vehicles involved traffic accidents

The increasing popularity of electric vehicles (EVs) has given rise to concerns regarding emerging safety challenges. To address this, an enhanced spatio-temporal kernel density estimation (STKDE) method is developed, which leverages GIS technologies to analyze the temporal evolution of EV accidents across different locations. Our approach introduces several innovations: (1) The incorporation of network distance to address overestimation associated with the conventional Euclidean distance; (2) Integration of a Severity Index (SI) to rationalize accident weighting; (3) Utilization of a likelihood cross-validation method to determine optimal bandwidths for simplified computation. Additionally, statistical significance tests and a ranking system based on cluster strength are implemented to enhance the accuracy and stability of hotspot identification compared to the traditional kernel density estimation (KDE) method. Empirical analyses reveal spatial trends, indicating a higher likelihood of EV accidents occurring near city administrative centers and along arterial roads. Temporally, our findings show increased daytime hotspot density with distinct morning and evening peaks compared to nighttime. Furthermore, a total of 209 hazardous locations, containing 907 accidents, representing 23.4% of all accidents are filtered. Our improved STKDE approach demonstrates a mean hit rate of 0.553 and a PAI of 5.573, which are 3.5% and 27.5% higher, respectively, than those achieved with SKDEKDE. These insights can assist transportation agencies in implementing targeted interventions and resource allocation strategies.

Spatial and Temporal Characterization of Mine Water Inrush Accidents in China, 2014–2022

Currently, water inrush accidents in China’s coal mines are mainly under control, but occasionally, grave water inrush accidents still occur, causing significant casualties and economic losses. Existing studies have primarily focused on accident statistics, and the research on the trend of accident evolution is becoming obsolete to match the current context of coal resource development. This study analyzes the water inrush accidents in China’s coal mines between 2014 and 2022. It investigated the spatial and temporal distribution of accidents, the level of accidents, and the extent to which water prevention and control measures vary by zone in coal mines. The study results showed that from 2014 to 2022, water inrush accidents in coal mines exhibited a “decline-stability-fluctuation” stage change pattern. Additionally, the location of water inrush accidents has shifted westward. Paying particular attention to preventing and controlling water disasters in coal mines within the west development zone is crucial. The water disaster accidents in China’s coal mines have been effectively controlled, but the problem of unequal levels of preventing and controlling water disasters in coal mines persists. This study can provide a reference for the safe and efficient production of coal mines and the control of the number of deaths in mine water hazard accidents.

Time-dependent earthquake-fire coupling fragility analysis under limited prior knowledge: A perspective from type-2 fuzzy probability

Earthquake-triggered fire domino scenarios (E-FDSs) arise frequently from the interaction between earthquakes and chemical installations, resulting in catastrophic multi-hazard coupling events. The complicated mutually amplified phenomena between natural disasters and chemical accidents significantly aggravates the escalation of domino accidents, which has posed great challenges for modeling and preventing E-FDSs. Under this impetus, this work proposes an advanced type-2 fuzzy probabilistic methodology to obtain the time-dependent failure probability of steel cylindrical tanks (SCTs) subjected to the earthquake-fire sequence. To cope with the limited prior knowledge on E-FDSs, a basic universal is established to describe the fire resistance attenuation caused by the seismic damage. The coupling failure criterion of SCTs is formulated by a type-2 fuzzy time-dependent limit state equation. A credibility-based stochastic simulation algorithm is developed for the hybrid uncertainty analysis (combining ambiguity and stochasticity). The proposed methodology is validated by case studies of a 5000 m3 fixed roof tank. Compared to the existing accident probability model, the proposed methodology can not only capture the fire resistance attenuation caused by the seismic damage but also provide a dynamic estimation of tank failure probability with respect to the fire exposure time. The proposed methodology can effectively and dynamically capture the accident evolution process, which in turn helps mitigate and prevent the spatiotemporal propagation of domino effects.

Temporal evolution of work accidents in Santa Catarina construction industry

Temporal evolution of work accidents in Santa Catarina construction industry

Modeling the evolution of industrial accidents triggered by natural disasters using dynamic graphs: A case study of typhoon-induced domino accidents in storage tank areas

Modeling the evolution of industrial accidents triggered by natural disasters using dynamic graphs: A case study of typhoon-induced domino accidents in storage tank areas

Agent-based modeling methodology and temporal simulation for Natech events in chemical clusters

In chemical clusters, technological accidents triggered by natural events (Natech events) can lead to the failure of large number of units and cause catastrophic consequences. Previous studies on Natech events give primary concerns on the static analysis at small-scale region with few thoughts the impact of various safety barriers. From the perspective of the large-scale region and temporal characteristics, an agent-based modeling methodology is proposed to present the dynamic evolution process. All the units associated with the evolution are divided into natural hazard agent, hazard-affected facility agent, safety barrier agent, and escalation factor agent to reduce the computational complexity. The time-dependent state transition mechanism of each agent is analyzed to represent the evolution process, simultaneously, the temporal effect of natural hazards, safety barrier and multi escalation factors is considered by utilization of time steps. Moreover, Monte Carlo simulation is adopted to quantify the uncertainties accompanying an accident evolution. The proposed methodology is demonstrated on a complex chemical system in the large-scale region. The results of simulation show that the number of failed hazard-affected facilities in the Natech events scenario will cyclically increase and keep constant, with strong temporal characteristic. The intensity of natural hazards and the probability of failure of safety barriers make a big impact on the temporal characteristic. The results are valuable to support natural hazard prevention in chemical cluster.

Lessons and improvements: Subway waterlogging catastrophe in Zhengzhou, China

The cause of major accidents provides valuable lessons for hazard prevention and management improvement. However, the investigation of major accidents in a social-technical system could be a challenge that requires inter-disciplinary approaches. Accimap, a system-oriented method in accident investigation, has been widely used around multiple industries. However, as a typical social-technical system with various internal and external interactions, system-oriented analysis has rarely been applied in subway systems. Therefore, the objective of this work is to enhance the Accimap framework to conduct a hierarchical investigation into the contributions of multiple factors to the Zhengzhou waterlogging accident and analyze the interconnections between factors during the accident’s progression. To capture the multiple-stage characteristic of subway accidents, a sub-system concept is introduced. By studying the accident evolution in depot/construction subsystem, station/dispatch subsystem and tunnel/rescue subsystem, this work provides a detailed case study that reveals the underlying deficiencies in the emergency management of Chinese subway system. Besides, this work also reviewed the most important countermeasures and policies implemented by different levels of governments following the accident. By demonstrating the lessons learned from and improvements made after the major accident, the findings can support the accident prevention and policy making within the subway industry in the future.

On the development and measurement of human factors complex network for maritime accidents: A case of ship groundings

Human factors from organizational aspects to unsafe behaviors of the crew on board form a directed complex network reflecting the evolution of maritime accidents. The present study aims to develop a human factors complex network contributing to maritime accidents. First, a maritime accident scenario is defined based on 104 ship grounding accident reports, then the human factors contributing to ship groundings are identified and structured within HFACS using grounding theory. Second, chi-square tests and odd ratio analysis are employed to capture the interrelationships among these human factors within HFACS, which are then represented as nodes, edges, and edge weights in network. The topological parameters including degree, clustering coefficients, and node importance are measured in the end for the human factors complex network. The results indicate that the factor “Inadequate safety management by the shipping company” belonged to Organizational influences has the highest influence on the factors “Not taken safe ship speeds”, “Improper lashing and stowage”, “Failure to implement the planned route”, and “Failure to keep navigational AIDS in normally open operation’ that belonged to Unsafe acts, while these unsafe acts have the highest PageRank value, affirming their direct role in causing maritime accidents. Results from this study are valuable for understanding of the network propagation mechanism of human factor risks in maritime accidents, and can also offer guidance to shipping companies, maritime authorities, and relevant stakeholders in establishing and optimizing human factor barriers in such accidents.

Descriptive analysis and a proposal for a predictive model of fatal occupational accidents in Spain

Accidents at work are a problem in today's economic structures, but if they result in the loss of human lives, the economic and social cost is even higher. The development of prevention policies, both at governmental and sectoral level, has led to a progressive reduction of occupational accidents, but number of fatal accidents remain high. The aim of this study is to explore the evolution of fatal accidents at work in Spain for the period 2009–2021, analyse the relationship between the main variables, and propose a predictive model of fatal occupational accidents in Spain. Data for this study are collected from occupational accident reports via the Delt@ (Electronic declaration of injured workers) IT system. The study variables were classified into five groups: temporal, personal, business, circumstances, and consequences. Fatal accidents at work are more common in males and in older workers, especially in workers between 40 and 59 years old. Companies with less than five workers have the highest percentage of fatal accidents, and the transport subsector and that the worker is carrying out his/her usual work have a strong correlation in the fatal accidents. Results can help to the agents involved in the health and safety management to develop preventive measures, and action plans.

Dynamic risk investigation of urban natural gas pipeline accidents using Stochastic Petri net approach

The safety of urban gas pipelines is challenged by a series of adverse factors, and the unexpected accidents may pose a catastrophic threat to humans, the environment and assets. The existing studies mainly focused on causation investigation of natural gas pipeline accident, the dynamic evolution process of urban natural gas pipeline accident is still challenging task for accident prevention. To find out the unfavorable factors that cause accidents, this study presents a dynamic risk modeling of urban natural gas pipeline accidents using Stochastic Petri net (SPN). An SPN model of an accident evolution process is constructed based on the discrete events in an accident flowchart, and the critical places and transitions are evaluated through this model. Considering the slow development of the early events leading to accidents, the delay time of the transitions at this stage cannot be determined, Bayesian theory is used to dynamically update SPN model. Critical accident nodes and their occurrence probabilities are estimated, which are used to support the efficient risk management strategies. The gas explosion accident in Shiyan, Hubei, as a representative case is investigated, and the results show that pipeline corrosion, ignition sources, inefficient information feedback and unreasonable solutions are critical accident causations. The probabilities of critical accident nodes increase over time, which means that these factors need to be managed efficiently to prevent the accidents. The application shows that the model can be used as a tool for gas companies and governments to investigate urban gas pipeline accidents.

Occupational Accidents in Ecuador: An Approach from the Construction and Manufacturing Industries

Ecuador is a country in South America, where, in 2021, there were 849,874 companies that had total sales of USD 105.23 billion, which had 2,698,650 workers. This study was carried out with the aim of analyzing occupational accidents by industry type in Ecuador, focusing mainly on the construction and manufacturing industries. The data were provided upon express request to the National Directorate of General Occupational Risk Insurance of the Ecuadorian Social Security Institute. The data used in this study concerned accidents involving qualified workers affiliated with the Ecuadorian Social Security System. The data obtained were used to calculate the general accident rate, accident rate by sector, permanent disability rate, and mortality rate. Between 2016 and 2019, 6960 qualifying accidents occurred in Ecuador. Approximately 89.71 ± 0.36% of the accidents investigated involved men, and 50.28 ± 2.49% of these accidents caused permanent disability. The mortality rate was, on average, 10.60 ± 1.83%, 2018 being the year with the most deaths due to accidents at work. The Ecuadorian sector with the highest accident rate was the manufacturing industry with 26.15 ± 2.50%, followed by the construction industry with a rate of 17.47 ± 3.59%. The manufacturing industry had the highest rate, with a significant difference of accidents that resulted in permanent disability, with an average of 31.79 ± 2.85%, while the construction sector had the highest rate of fatal accidents, with an average of 25.82 ± 5.05%. This is the first study to report the number of qualified occupational accidents in Ecuador. With this study, a starting point is established for future analyses of the evolution of occupational accidents by type of industry in Ecuador. Future studies could determine the increase or decrease in the number of accidents, evaluate rates of permanent disability and death, and establish risk factors and preventive measures for each task or activity in all productive sectors.

Large-scale transient simulation for consequence analysis of hydrogen-doped natural gas leakage and explosion accidents

Transporting blended hydrogen natural gas through existing natural gas pipeline networks is an important strategy for meeting the growing demand for hydrogen energy. However, gas leakage poses a serious safety concern. This study conducts a large-scale simulation of gas leakage and explosion accidents in a hydrogen-doped natural gas station and aims to evaluate the impact of pipeline pressure and leakage direction on the accident consequences. Simulation results indicated that the high pipeline pressure led to more accumulated flammable gas, which caused severer explosion disasters. Within the 100s simulation period, the cumulative volume of the flammable gas cloud reached 2329.80 m3 under 4 MPa pipeline pressure, and the peak explosion overpressure increased by 69.64% compared with the 1 MPa case. Leakage directions also significantly affect the evolution of accidents. Suppose the leakage direction is towards living areas, flammable gas is more likely to accumulate in confined spaces up to 2363.20 m3. The gas explosion will severely damage the building structure with a 17 kPa explosion overpressure and 7 kPa shock wave intensity. Interaction with wind and the disturbance of obstacles also contributed to the flammable gas cloud accumulation, which increased peak values of explosive overpressure.

Dynamic risk analysis of flammable liquid road tanker based on fuzzy Bayesian network

AbstractDue to environmental variability of combustible liquid road transportation, it is difficult to analyze risk accurately. This article proposes a method for real‐time analysis of the risk of flammable liquid road transportation using a fuzzy Bayesian network. This method combines the binary logistic regression model with the bow‐tie model to analyze the influencing factors and accident evolution. Then, a Bayesian network framework is established based on the fault tree model. In the case of limited historical accident statistics, the fuzzy set theory and Leaky Noisy‐OR theory are used to determine the prior probability and conditional probability of Bayesian network nodes, and ALOHA software is used to simulate the dangerous area of the accident to define further the safe evacuation range and emergency rescue material demand. Taking the road transportation of a tanker in Beijing as an example, the accident probability is updated in real time by monitoring the data of transportation nodes, and the change rule of risk level with internal and external conditions at different times is analyzed. The research shows that this method can dynamically describe the road transportation accidents of hazardous chemicals with the GPS data of vehicles.

3D Transient CFD Simulation of an In-Vessel Loss-of-Coolant Accident in the EU DEMO WCLL Breeding Blanket

The in-vessel Loss-of-Coolant Accident (LOCA) is one of the design basis accidents in the design of the EU DEMO tokamak fusion reactor. System-level codes are typically employed to analyse the evolution of these transients. However, being based on a lumped approach, they are unable to quantify localised quantities of interest, such as local pressure peaks on the vacuum vessel walls, to which the failure criteria are linked. To calculate local quantities, the 3D nature of the phenomenon needs to be considered. In this work, a 3D transient model of the in-vessel LOCA from a water-cooled blanket is developed. The model is implemented in the commercial CFD software STAR-CCM+. It simulates the propagation of the water jet in the vessel from the beginning of the accident, thus accounting for the phase change of the water, i.e., from the pressurised liquid phase to the vapour phase inside the vessel, being the latter at a much lower pressure than in the blanket coolant pipes. Due to the large pressure ratio (>1000), shocks are expected; therefore, an Adaptive Mesh Refinement (AMR) algorithm is employed. The physical models (in particular, the multiphase model) are benchmarked to a 2D reference problem before being applied to the 3D EU DEMO-relevant problem. The simulation results show that the pressure peaks in front of the vessel walls are not dangerous as they are below the design limit. The entire evolution of the water jet is followed up to the opening of the burst disks, in order to compare the average pressure evolution with that computed with system-level codes. A comparison with the in-vessel LOCA from a helium-cooled blanket is also carried out, showing that the accident evolution in the water case is less violent than in the helium case.