Accident Model Research Articles

A numerical study on metallic melt infiltration in porous media and the effect of solidification

The melt infiltration in porous debris is of importance to severe accident prediction and mitigation in nuclear power plants (NPPs), but its mechanism remains elusive. In this study, a computational fluid dynamics (CFD) model is proposed to simulate the evolution of melt infiltration within porous media, incorporating both solidification and melting processes. The CFD model is validated against the experiment (REMCOD facility) and Moving Particle Semi-implicit (MPS) simulation results. Building upon this validated model, the influence of the melt superheat, the initial particle temperature, and its surface wettability on melt infiltration dynamics are mainly analyzed. It is found that increased initial melt superheat enhances melt infiltration length and rate; higher initial particle temperatures promote deeper and faster infiltration, while lower temperatures may result in solidification that blocks further infiltration. Additionally, the wettable particulate bed can enhance melt relocation and heat transfer, but it also accelerates the solidification of the melt, which complicates the infiltration process. Furthermore, phase changes could intensify melt flow instability. This work may expand our understanding of melt infiltration dynamics and pave the way to severe accident modeling in NPPs.

Development of an Optimized Accident Prediction Model for Construction Safety Using Big Data Analysis

Background and objective: The objective of this study was to develop an intuitive accident prediction model that can be readily applied at construction sites to effectively reduce the incidence of accidents through the analysis of construction accident data.Methods: These accidents significantly contribute to the construction industry's overall accident rate. Accident types were categorized into fatalities, injuries, and material damages to construct the accident prediction model. A total of 24 factors were considered across eight major variables, which were identified during the first and second phases of data preprocessing to analyze construction accident big data. Machine learning techniques were employed, specifically supervised learning and ensemble learning, to identify the optimal predictive model.Results: Among the models tested, XGBoost emerged as the most effective due to its highly balanced accuracy, even in the presence of class imbalance.Conclusion: The implementation of the XGBoost accident prediction model, along with the feature importance codes developed in this study, enables the prediction of accident types for specific tasks performed at construction sites. This predictive capability is expected to inform the implementation of targeted accident prevention measures, such as enhancing safety protocols or adjusting work procedures based on the prediction outcomes.

Research on fire accident prediction and risk assessment algorithm based on data mining and machine learning

Forest fire is a kind of natural disaster that is destructive, easy to spread, and difficult to extinguish. It greatly harms the balance of ecosystem and human life and property. The prediction and risk assessment of forest fire accidents can find forest fires as early as possible and then take corresponding remedial measures; the loss of forest fires will be minimized. This paper first collected the relevant data information in the Lesser Hinggan Mountain region, established the fire driving factor database, and then analyzed the impact and distribution characteristics of forest fire driving factors such as temperature and rainfall. Finally, a fire accident prediction model was built based on a deep neural network, and the model’s performance was compared with the SVM and RF models. The analysis results show that this paper’s fire accident prediction model is more accurate.

Machine learning and multisource data analysis approach towards traffic accident risk prediction

Traffic accidents are continually a concern across the world bringing with them huge economic costs and high rates of fatality and injuries each single year. Predictive modeling can lessen such risks if the accident hotspots and timing can easily be ascertained. This study collects various data including historical accident reports, water and meteorological conditions, road characteristics, and telematics data, to form traffic accident risk predictive models. To construct and analyze these predictive models, we use various machine learning techniques such as logistic regression, decision trees, and ensemble methods (Random Forest and Gradient Boosting). The results bear out as efficient and effective, ensemble models are streets ahead than regression-based techniques with Gradient Boosting achieving AUC-ROC scores of up to 0.89. The results of this research can also assist in applying effective safety measures in a more cost-effective manner as well as facilitate the development of efficient transport management systems and policies.

Analysis of causes of traffic accidents for taxi drivers based on MIMIC and machine learning

Through questionnaire survey, information on individual attributes, physical fatigue perception, work pressure, risky driving behavior and traffic accident experience of 2391 taxi drivers was collected. The multi-indicator multi-cause (MIMIC) model was used for path analysis to explore the inducing effect of physical fatigue perception and risky driving behavior on traffic accidents, and to verify the influence of gender, age and work pressure on physical fatigue perception and distracted driving behavior. Four machine learning algorithms, logistic regression, naive Bayes, support vector machine and random forest, were selected to predict taxi accidents. The results show that the increase of physical fatigue perception, negligent driving behavior, aggressive driving behavior and distracted driving behavior can lead to an increase in accident rate, and gender, age and work pressure will affect the frequency of physical fatigue perception and distracted driving behavior. The accident prediction model based on machine learning has excellent effect, among which the random forest has the best prediction effect. The prediction accuracy of using single feature variables "risky driving behavior", "distracted driving behavior" and "physical fatigue perception" is acceptable. When the personal attribute indicators of "work pressure", "age" and "gender" are introduced, the prediction accuracy is further improved.

Road section traffic risk propagation model based on macro traffic flow energy consumption

Traffic risk is an important source of road traffic accidents. Effectively predicting the propagation of accident risks on the road after an accident is of great significance for preventing secondary accidents. Therefore, this paper establishes a road section accident risk propagation model based on macro traffic flow energy consumption. Firstly, the influence of lane occupation and vehicle lane changing after the accident on traffic flow is analyzed, and a macro traffic flow model under the risk after the accident is constructed. Secondly, combined with the definition of traffic flow energy dissipation, the movement of the whole vehicle is abstracted as a fluid movement process, and a traffic flow energy consumption model under the risk after the accident is proposed to quantify the accident risk. Finally, according to the spatiotemporal correlation of risk propagation, a risk impact range and duration calculation model is proposed to describe the risk propagation process. In order to verify the effectiveness of the risk propagation model proposed in this paper, MATLAB and VISSIM are used to carry out numerical simulation and simulation verification of traffic flow operation under different initial traffic flow densities. The experimental results show that the greater the initial density of the road section when the accident occurs, the wider the risk impact range and the longer the duration. The absolute value of the prediction deviation rate of the proposed model is below 8%, which has a good prediction effect.

Towards safer mining environments: an in-depth review of predictive models for accidents

Towards safer mining environments: an in-depth review of predictive models for accidents

Urban Natural Gas Pipeline Operational Vulnerability Under the Influence of a Social Spatial Distribution Structure: A Case Study of the Safety Risk Patterns in Kunming, China.

Frequent urban natural gas pipeline accidents pose a serious threat to the safety of people and property in surrounding areas. However, current research on natural gas pipeline risks primarily focuses on evaluating the pipelines themselves, with no established method for assessing the impact of pipeline disasters on surrounding areas. This paper proposes an urban natural gas pipeline risk assessment method that integrates the physical attributes of the pipelines with an analysis of social vulnerability based on urban social spatial distribution. Using urban Point of Interest (POI) data, a social spatial distribution model for potential natural gas pipeline accidents is constructed. The risk of pipeline failure is assessed based on physical vulnerability, while the consequences of failure are evaluated through social vulnerability. This method combines the analysis of physical and social vulnerabilities to achieve a comprehensive urban natural gas pipeline risk assessment. The results identified 68 out of 6148 pipelines in the study area as "double high" pipelines, characterized by high physical vulnerability (relatively high risk pipelines) and high social vulnerability (involving level IV areas). The high risk communities identified in the study area are the Cuihu West Road Community and the Daguan Commercial City Community, highlighting the characteristics of risk distribution. The findings suggest that this study contributes to improving urban resilience to natural gas pipeline incidents, reducing potential economic losses and public impacts, and enhancing urban public safety. It also provides new insights into natural gas pipeline risk assessment and urban public safety research.

A risk assessment model of spontaneous combustion for sulfide ores using Bayesian network combined with grounded theory

Sulfide ores, collectively referred to as a class of minerals with extensive applications, are increasingly susceptible to spontaneous combustion incidents as the mining environment evolves. The spontaneous combustion of sulfide ores is a complex nonlinear process, and the mechanisms of which remain not fully understood. Consequently, it is challenging to delineate the accident trajectory through traditional accident causation models and subsequently construct a probabilistic model for spontaneous combustion risk. To address this issue, this paper proposed a risk assessment model based on grounded theory and Bayesian network. The advantage of this model is that it employs grounded theory to categorize and organize factors influencing the occurrence of accidents, thereby forming a clear accident trajectory and providing a framework and basis for constructing a probabilistic assessment model. Furthermore, the model presented in this paper introduces the D number theory and the Noisy-OR model to handle uncertainties in the risk assessment process and to establish more rational conditional probability tables. The findings suggest that environmental temperature, heat dissipation conditions, safety awareness, and safety skills are the most influential factors in spontaneous combustion incidents when considering a particular point in time. However, when considering the entire process of sulfide ore pile spontaneous combustion, the temperature of the ore pile is the most critical accident factor to monitor. Therefore, the key to suppressing the spontaneous combustion of sulfide ores lies in reducing environmental temperatures, enhancing heat dissipation, and elevating the safety awareness of relevant personnel.

Vulnerability assessment of buildings adjacent to natural gas pipelines under explosion accident

ABSTRACT Natural gas pipeline leak may lead to explosion accidents, which may threaten the safety of the adjacent buildings. This paper conducts a vulnerability assessment of buildings adjacent to natural gas pipeline under explosion, which is implemented by Copula Bayesian network (CBN) model. The incident escalations and evolution nodes were identified based on the accident reports. The correlation between nodes is calculated using the Kendall correlation coefficient to develop a CBN model of natural gas pipeline accidents. The weaknesses in the buildings adjacent to pipelines are identified using spider diagrams. The vulnerability of building adjacent to pipelines is characterized by the damage degree. The model is verified by a case study. The results indicate that the occurrence probability of the explosion accident reaches 85%. The identified weaknesses are ignition sources and confined spaces. The occurrence probability of a delayed ignition within buildings is 0.23, and the explosion overpressure is estimated to be 84.25 kPa. The vulnerability degree of building 1 is higher than building 2. These findings can help to understand the system weaknesses and support the safety improvement of buildings in the case of natural gas pipeline explosion.

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.

Proposal of a workplace classification model for heart attack accidents from the field of occupational safety and health engineering

Research on occupational accidents is a key factor in improving working conditions and sustainability. Fatal accidents incur significant human and economic costs. Therefore, it is essential to examine fatal accidents to identify the factors that contribute to their occurrence. This study presents an overview of fatal heart attack accidents at work in Spain over the period 2009–2021. Descriptive analysis was conducted considering 13 variables classified into five groups. These variables were selected as predictors to determine the occurrence of this type of accident using a machine learning technique. Thirteen Naïve Bayes prediction models were developed using an unbalanced dataset of 15,616 valid samples from the Spanish Delta@database, employing a two-stage algorithm. The final model was retained using a General Performance Score index. The model selected for this study used a 70:30 distribution for the training and test datasets. A sample was classified as a fatal heart attack if its posterior probability exceeded 0.25. This model is assumed to be a compromise between the confusion matrix values of each model. Sectors with the highest number of heart attacks are ‘Health and social work’, ‘Transport and storage’, ‘Manufacturing’, and ‘Construction’. The incidence of heart attacks and fatal heart attack accidents is higher in men than in women and higher in private sector employees. The findings and model development may assist in the formulation of surveillance strategies and preventive measures to reduce the incidence of heart attacks in the workplace.

An accident severity prediction framework with consider features interaction

Accident severity prediction is increasingly important for preventing and reducing traffic accident losses. However, many studies always ignore the complex relationship between features during feature selection. To improve the prediction accuracy of the accident severity prediction model, this paper considers the interactions between multiple features. First, the feature selection algorithm of Recursive Feature Elimination with Cross-Validation (RFECV) is improved by using Shapley Additive explanations (SHAP) as the feature importance assessment metric. Then, to decrease the time expense of manually finding hyperparameters of the model, the Hunter Prey Optimization (HPO) algorithm is introduced and logistic mapping together with stochastic perturbation is added to it, which makes it easier to skip out of the partial optimum during the optimization search. Finally, the improved HPO algorithm is used to optimize the hyperparameters of the CatBoost model. The US traffic accident dataset is introduced for the validity of the proposed framework. Experimental results show that the proposed framework achieves a prediction accuracy of 96.63%, which is better than other state-of-the-art methods. The high accuracy of the prediction model can help decision-makers develop more rational transportation policies, and this study also proposes some traffic management measures based on the selected features.

Analysing Near-Miss Incidents in Construction: A Systematic Literature Review

The construction sector is notorious for its high rate of fatalities globally. Previous research has established that near-miss incidents act as precursors to accidents. This study aims to identify research gaps in the literature on near-miss events in construction and to define potential directions for future research. The Scopus database serves as the knowledge source for this study. To identify publications on near-miss events, the search field “Article Title, Abstract, Keywords” was utilized with the keywords “construction” and “near miss”. The main research themes were defined based on keyword mapping performed using VOSviewer. Selected publications were assessed for their alignment with the defined research theme. A statistical analysis of the publications and the co-occurrence of keywords was conducted. The authors of the identified publications primarily used statistical analyses, artificial intelligence, employee monitoring, tracking systems, and building information modelling in their research. The conclusions from the literature review indicate a need for further research focused on developing effective predictive models for workplace accidents based on knowledge of near-miss events. This will contribute to a better understanding of the mechanisms leading to accidents and their prevention, ultimately resulting in a significant reduction in accidents in the construction sector.

In-situ ETEM study of plasma-facing tungsten nanofuzz oxidation at atmospheric pressure: Microstructure evolution and substrate-free oxidation kinetics

To enable sustainable carbon-free fusion energy, managing reactor structural material degradation during normal operation as well as accident scenarios is vital. Tungsten (W) plasma-facing materials (PFMs) are susceptible to aggressive high-temperature oxidation during air-ingress fusion reactor accidents, yet there's a lack of oxidation kinetic data for irradiated tungsten. Here, we utilize atmospheric environmental transmission electron microscopy (ETEM) to present the first kinetic data for substrate-free W nanofuzz oxidation at 400 °C and 500 °C in 1 bar dry air. Comparison with pristine bulk W during the early parabolic stage suggests an irradiation-decelerated oxidation for W nanofuzz. Our time-resolved in-situ characterization reveals a durable amorphous surface oxide, likely promoted by high-flux He+ irradiation-induced surface defects, serving as an effective passivating layer that impedes nanofuzz oxidation onset. This surface oxide layer also interfaces well with newly formed orthorhombic WO3, facilitated by stress relief through He bubble shrinkage, providing lasting passivating protection throughout the nanofuzz parabolic oxidation. This new finding challenges conventional notions of irradiation's negative impact on metal oxidation, and calls for advanced characterization to enhance our understanding of fusion energy materials degradation, informed by further accident modeling.

A new paradigm for construction safety management in China: Introducing knowledge graph and accident database into the early-stage of BIM

Significant progress has been made in managing construction safety risks based on Building Information Modelling (BIM) technology. BIM is active in all stages of construction, including5 planning, design, pre-construction and construction. However, the causes of risks and accidents are complex and can be related to humans, the environment, data and project portraits. The current construction safety risk management process lacks scientific reference and efficiency in conducting comprehensive inspections for specific projects. The uneven and substandard data quality in BIM models significantly undermines their effectiveness. This article introduces a knowledge graph and accident database through data crawling and structuring, with the use and analysis of an accident causation model, providing key risk elements and management directions for safety managers who require a high level of attention during the early-stage of BIM. The obtained knowledge graph was subjected to data mining analysis to explore the causes of accidents, considering the combined effect of multiple factors, such as “case-date-cause” and “case-address-cause”. The paradigm can be recognized by the BIM, and through the secondary development on the development side, a data interface is constructed to transfer the knowledge graph into the BIM. The approach mentioned in the article can provide standard and structured data for the BIM model, analysing high-risk points of projects, which is valuable for engineering applications.

Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach

Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.

TIME SERIES MODELLING OF ROAD TRAFFIC ACCIDENTS IN WEST ARSI, ETHIOPIA

The aim of this research is time series modeling of road traffic accidents in the west Arsi zone, Ethiopia, it focused on monthly traffic accidents from January 2016 to December 2020. The goal of this study was to explore the number of traffic accidents to fit a time series model for the monthly number of road traffic accidents and to forecast a tow year ahead of the number of road traffic accidents. The analysis was done by using statistical software packages using this software and knowledge of time series analysis, trend, ACF, PACF, and Box-Jenkins analysis were computed. From the trend plot, the road traffic accidents was fluctuate from month to month as well as from year to year. There was total accident fluctuation from month to month (not stationary) a total of 1010 RTA were observed. The mean of 16.83 and standard deviation of 5.764 for the total accident was served the minimum and maximum record of road traffic accidents is 4 and 33 respectively. By differencing data one time, (2, 1, 3) model was fitted for making a two-year ahead forecast. Proper model adequacy checking was done. Two-year ahead forecasts showed that October, January, and April 2021 are the months with the most prominent values. Even if the trend in total accidents was decreasing there is still a need to pay more attention in order to prevent the occurrence of accidents related to road traffic accidents.

Building reliability of risk assessment of domino effects in chemical tank farm through an improved uncertainty analysis method

The severity and complexity of fire-induced domino effects have received widespread attention. The spatial and temporal evolution process modeling of domino accidents is still stuck in single crisp value, in which the uncertainty will affect the reliability of the results. In this paper, we propose a computational method for uncertainty propagation of the evolution process modeling of the domino effect called Uncertainty Propagation of Domino Evolution Model (UPDEM), aiming to increase the reliability of risk assessment results. The method is capable of obtaining ranges and corresponding probability envelopes for tiame to failure and escalation probabilities, and quantifying uncertainty for all computational models that require input parameters. The uncertainty is quantified based on the evidence theory and a parameter cropping method based on the stress-strength interference model is proposed to optimize the evidence theory. The proposed method is applied to a case study and compared with previous studies. The comparison confirms that the results are more reliable after considering uncertainty and can produce the worst possible scenario consequence. Conducting the sensitivity analysis to prioritize the treatment of parameters and identify effective measures in risk control. The methodology can provide an important reference for decision-makers to prevent and control domino effects.

Development of a Sports Safety Promotion Framework Based on KABP Theory and 4M Management

This study explores the significance, current research landscape, and conceptualization of sports safety promotion. Safety in sports is fundamental to youth physical activities, and an excessive focus on or neglect of safety is unwarranted. Globally, numerous countries have extensively researched sports safety promotion and implemented diverse strategies. Drawing from KABP (Knowledge, Attitude, Behavior, Practice) theory and 4M (Man, Machine, Medium, Management) management, this paper presents a conceptual framework for sports safety promotion. It integrates these theories to devise a comprehensive accident prevention model within a sports safety promotion system. The framework prioritizes enhancing students’ safety literacy and underscores the practical application of safety knowledge and skills in simulated sports settings following structured safety education. It aims to enhance students’ competency and proficiency in averting sports-related injuries.