Causes Of Accidents Research Articles

Ship Bridge OOW Activity Status Detection Using Wi-Fi Beamforming Feedback Information

Officers on Watch (OOWs) of the ship’s bridge play a vital role in maritime navigation safety, monitoring the ship’s navigational status, and ensuring maritime safety. The status of inactive watch officers, such as fatigued driving and negligence on lookout, is one of the main causes of accidents. Intelligent technology for real-time perception and state evaluation of ship OOWs significantly reduces accidents caused by human factors. The traditional computer vision method is difficult to adapt to the complex environment of a ship bridge, and carries strong privacy risks. With the development of Internet of Things technology, sensing technology based on ubiquitous Wi-Fi devices provides a new way to accurately monitor the status of ship OOWs. In this paper, we use commercial off-the-shelf (COTS) Wi-Fi devices to propose a ship driving activity state detection method based on beamforming feedback information (BFI). Using wireless sensing data to sense the number of OOWs and their driving behavior realizes low-cost and high-precision detection of the behavioral status of the ship’s bridge watchkeeper. Experiments were conducted in a ship-driving simulation laboratory and on a real-world Yangtze River cruise ship. The experimental results demonstrate that our proposed method achieves 92.4% and 98.1% accuracy for tracking active status and estimating the number of OOWs, respectively.

Pengendalian Bahaya dan Risiko K3 Menggunakan Metode HIRADC dan FTA Pada Industri Kerupuk

CV Irma is one of the traditional cracker processing industries located in the city of Bekasi. The production process of processing these crackers is still carried out traditionally. This can cause gaps in the occurrence of work accidents due to processes and ways of working that still use manual processes. The purpose of this study is to provide proposals for K3 risk control to minimize the potential dangers and risks that exist in the cracker production process. The study was conducted using the HIRADC (Hazard Identifcation, Risk Assessment and Determining Control) and FTA (fault tree analysis) methods to determine the root cause of a kerrja accident with a high risk value. The results of this study were obtained to be able to reduce the risk level of the existing potential hazards, from the high, medium, and low value risk levels from the comparison before and after control.

A Literature-Based Framework for Analysing Fall-From-Height Accidents and Safety Preventive Measures in the Construction Industry

Fall-from-height accidents are a significant cause of injuries and fatalities in construction industries. The occurrence of this accident may have been caused by any number of contributing factors. In addition, various preventive measures have been proposed to prevent this accident from occurring. To capture the true condition of a fall from height accident, the causes of the accident, the effects of the accident, and the preventive actions should be rendered out. Therefore, it is possible to analyse these accidents in order to determine their fundamental causes and implement effective preventative measures. This article presents a literature-based framework for analysing fall-from-height accidents. A literature review was conducted to examine existing studies and identify similar themes and patterns on fall from height accidents in the construction industry to illustrate the framework. Where the framework outlines the key factors that contribute to the accident. The framework comprises four main components: (1) fall from height accident, (2) causes of accident, (3) effect of accident, and (4) development of safety preventive measures. Along with the literature review that was carried out, an identification and outlining of the factors and subfactors of causes of accidents, effects of accidents, and preventative safety measures was taken out. The proposed framework provides a structured approach for analysing fall-from-height accidents, which can help organizations to identify the underlying causes of such accidents and implement appropriate measures to prevent them. The framework is flexible and can be adapted to suit the needs of other industries and organizations. The paper also discusses the future research directions.

Ten-Year Cardiovascular Risk Estimation through the Framingham Risk Score among Commercial Motor Vehicle Drivers with High and Low Risk of Obstructive Sleep Apnea

Abstract Objective Obstructive sleep apnea (OSA) and cardiovascular disease (CVD) are both major medical causes of road accidents among commercial motor vehicle (CMV) drivers. The present study aimed to determine the association of the ten-year risk of developing CVD and other cardiac risk factors with the risk of developing OSA among apparently healthy CMV drivers. Materials and Methods The present cross-sectional study was conducted on 315 male CMV drivers. Demographic characteristics were collected, and the Epworth Sleepiness Scale (ESS), the Snoring, Tiredness, Observed Apnea, High Blood Pressure, Body Mass Index (BMI) > 35 kg/m2, Age > 50 Years, Neck Circumference > 40 cm, and Male Gender (STOP-Bang) questionnaire, and International Physical Activity Questionnaire-Short Form (IPAQ-SF) were filled out, and the ten-year CVD risk was calculated through the Framingham Risk Score (FRS) for each individual. Laboratory tests also were performed. The participants were divided into two groups based on the STOP-Bang score and FRS, and other CVD risk factors were compared, accordingly. The Chi-squared test, the Student t-test, analysis of variance (ANOVA), and logistic regression were used for the analysis. Results The mean age of the drivers was of 45.57 (range: 30 to 70) years. In the univariate analysis, the mean age, years of driving experience, FRS, vascular age, blood pressure, diabetes mellitus, metabolic syndrome, ESS score, BMI, and circumference of the waist and hip presented a significant association with STOP-Bang score ≥ 3 (p < 0.05). The metabolic equivalents of task (METs) for total physical activity and walking (min/week), time spent sitting (h/day), lipid profiles, smoking history, vehicle type, and shift type were statistically similar in both groups. In the multivariate analysis, a high risk of OSA was independently associated with higher FRS (odds ratio: 1.92; p < 0.001). Conclusion The STOP-Bang score presented a direct correlation with the estimated ten-year CVD risk in CMV drivers. The high risk of developing OSA among CMV drivers is accompanied by higher CVD risk factors such as old age, diabetes, hypertension, BMI, and waist and hip circumference. The FRS was statistically higher in CMV drivers with high OSA risk.

Analysis of the causes and safety countermeasures of coal mine accidents: A case study of coal mine accidents in China from 2018 to 2022

Statistical data on coal mine safety accidents in China from 2018 to 2022 were obtained. Mathematical statistical methods were used to analyze the accident level, accident type, death toll, time, space, and other factors. Grounded theory (GT), case-based reasoning (CBR), analytic hierarchy process (AHP), interpretative structural model (ISM) and other methods were used to analyze the causes of coal mine accidents in detail, and the characteristics, accident-causing attributes and importance of coal mine accidents in China were explored. The results indicate that (1) according to the level and type of accident, the incidence of general accidents in coal mines is the highest, with both the number of roof accidents and the death toll accounting for the highest values. From the time and location of the accidents, coal mine accident-prone areas are concentrated in northern and southern China. May and July are accident-prone periods every year. (2) Using GT-CBR to analyze the causes of coal mine accidents, 18 key statements were extracted, and 11 initialization concepts were summarized. Ultimately, the causes were categorized into 4 core categories: human factors (H), machine factors (M), environmental factors (E), and organizational management factors (O). (3) A hierarchical structure model of the causes of accidents according to ISM was built. The weight and proportion of accidents and deaths were combined to evaluate the importance of accident-causing attributes. Human factor-induced accidents accounted for 71.6 % of all accidents, and the number of accident death caused by organization management factors was the largest, accounting for 54.8 %. Comprehensive analysis revealed that human and organization management factors are the most important causes of coal mine accidents

Exploring associations between accident types and activities in construction using natural language processing

Existing accident analysis methods often fail to integrate the relationship of construction accidents with construction activities, restrict the potential for visualizing the accidents frequency in Building Information Modelling (BIM). This study proposes a decision support system that provides visual insights, and the frequency of the core construction accidents in relation to typical construction activities. K-mean clustering and keyword-matching techniques are used to group the leading causes of construction accidents and then compare them with the construction activities. The average hit rate computed for construction accidents was 91%, whereas the hit rate calculated for construction activities connected to these frequent accidents was 75%. The classification models are integrated into the Power BI platform to offer decision-makers deeper insights regarding the relationship of prevalent construction accident types and associated activities. The practical application of the system is demonstrated with a case study, exemplifying the fall accident data integrated in a BIM environment.

A method determining critical operating parameters for landing aircraft based on runway pavement skid resistance

ABSTRACT Poor skid resistance of airport pavement is the critical cause of runway excursion accidents. While the current determination methods of skid resistance are limited to the independent operating parameters and the discontinuously monitoring the skid resistance, the empirically calculating critical operating parameters. In this study, an aircraft tire-fluid-airport pavement skid resistance model was developed using numerical simulation technology. The wide-body aircraft B777 was taken as an example, the available skid number (SN) was used to continuously track the skid resistance of pavement under various landing conditions. The combined effects of groove type, water film thickness and landing weight on the pavement skid resistance performance were evaluated when the pavement reached the maintenance threshold state. Results show that the skid resistance of the ungrooved pavement was subpar according to FAA. The skid resistance was positively connected with the grooved size and landing weight but negatively correlated with the water film thickness. Under certain operating conditions, the FAA maintenance threshold groove still meets the requirements for aircraft landing safety throughout the whole range of landing speeds. A real-time determination method of pavement skid resistance was proposed. It can guide pavement maintenance management departments to avoid runway excursion accidents.

ANALYSIS OF ACCIDENT DATA AT PT X FOR THE 2018-2022 PERIOD USING THE HFACS-MINING INDUSTRY FRAMEWORK METHOD

The mining industry is a high-risk industrial activity. Human factors have been identified as the most common cause of major accidents in the mining industry. The Incident Management System of PT X, which is a company in exploration, mining, processing, and marketing sector of copper, gold and silver concentrate, part of the state-owned mining holding enterprise Mining Industry Indonesia, documented 322 cases of accidents within the 2018-2022 period. This research aims to analyze accident data at PT X using the Human Factor Analysis and Classification System-Mining Industry (HFACS-MI) framework. Methods. This research collected qualitative data for 322 accident cases at PT X in 2018-2022 from the incident management system database categorized as recordable injuries. Factors causing the accident were coded using the HFACS-MI framework. Accident data analysis used descriptive statistics. Results. The study findings revealed that 84% of all accidents involved contractor workers and 16% involved permanent workers at PT X. The results of the analysis using the HFACS-MI framework showed that each layer or level contributes to accidents, namely external factors by 44%, organizational influence by 68%, unsafe leadership by 90%, preconditions for unsafe acts by 99%, and unsafe acts by 99.7%. Conclusion. These findings emphasize the need to reduce the number of human errors during mining operations to reduce the current accident trend. The HFACS-MI framework has proven to be a valuable tool for robust accident analysis of human factors in mining.

Railway accident causation prediction with improved transformer model based on lexical information and contextual relationships

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents.

A novel inherent safety index for reactive hazard evaluation integrating thermal and pressure effects

Chemical reactive hazard is one of the main causes for accidents in chemical production, inherent safety indexing methodology is considered the most effective method for reactive hazard evaluation. Traditional reactive inherent safety indices often neglect pressure-related risks in runaway scenarios. This work introduced a chemical reactive risk index (CRRI), filling the gap in pressure risk assessment. CRRI integrates a semi-quantitative method based on risk analysis principles, consisting of the probability of reaction runaway and the severity of its consequences. The consequence of the whole runaway process is evaluated, and the severity of thermal and pressure effects are considered comprehensively. The PVA, TBHP and PFA production processes are utilized as case studies to demonstrate the application process and evaluation effectiveness of CRRI. The results illustrate the superiority of CRRI in comprehensive assessment of reaction risks, indicating CRRI as a reliable safety assessment tool for chemical reactions, helping to select inherently safer reaction processes.

DROWSINESS PREVENTION SYSTEM FOR DRIVERS

The Anti-Fatigue Alert System is a vital invention meant to tackle the dangerous problem of driver sleepiness, which is a major cause of traffic accidents globally. This device uses a variety of behavioural and physiological markers to identify when a driver starts to become sleepy, giving early alerts to help avoid potential collisions. The alert system keeps track of the alertness levels of drivers using sophisticated sensor technologies and algorithms. When fatigue signals are recognised, it intervenes with an aural, visual, or vibrating signal. The creation, operation, and efficacy of anti-fatigue alert system in reducing the dangers of driver drowsiness are examined in this abstract, which highlights the essential role these devices play in improving traffic safety and preventing fatalities. Keywords: Fatigue, Alert system, Safety management.

Detection of Drowsy Drivers in Video Sequences via LSTM with CNN Features

Fatigued driving is one of the main causes of traffic accidents. This paper developed a method for sleep drive detection that makes use of the convolutional neural network (CNN), recurrent neural network (RNN), short-term neural network (LSTM), cascade, and cam switching algorithms. Using CNN, the algorithm initially extracts the driver's face traits. The RNN that predicts the driver's degree of tiredness is then fed these features. The technology tracks the driver's eyes and eyelids using algorithms called Cascades and Cam shifts. The accuracy of the sleepiness prediction is increased further with the usage of this data. Using datasets of video recordings of attentive and somnolent drivers, the system was assessed. The outcomes demonstrated how well the algorithm could identify sleepy drivers. This system may be able to stop sleepy driving-related accidents. Ten thousand videos of awake and sleepy drivers were used to train the algorithm. Drivers can be observed in real time by the system. If the system detects that the driver is sleepy, it has the ability to sound an alarm. The technology can be used to stop driving when sleepy from causing accidents. Keywords: Convolution neural network, Recurrent neural networks, long short-term memory, eye tracking, face detection, drowsy driver detection, deep learning, computer vision, machine learning, and deep learning.

The Pattern of Motorcyclists' Death Due to Accidents and a Three-year Forecast in East Azerbaijan Province, Iran: A Time Series Study

Introduction: In low- and middle-income countries, a large proportion of road users include pedestrians, cyclists, and motorcyclists, and nearly half of road traffic fatalities occur among motorcyclists. This study aimed to examine the pattern of motorcyclists' death due to accidents in East Azerbaijan, Iran between 2006 and 2021 and present a forecast. Methods: We used death data due to motorcycle accidents of Legal Medicine Department between 2006 and 2021. For time series analysis, the Box-Jenkins model was used and three stages of identification, estimation, and diagnosis were successively performed and repeated several times to achieve the best prediction model. The Box-cox transformation method was used to stabilize the variance, and the first-order seasonal differential method with a period of 12 was used to control the seasonality. Due to seasonal variations, the Seasonality Auto-Regressive Integrated Moving Average model: SARIMA (p, d, q) (P, D, Q)s was employed and the death trend was predicted for 36 months. The candidate models were compared based on Log-likelihood, AIC, and BIC indices. STATA 17 was used for data analysis. Results: About 18.6% of all accident deaths are attributed to motorcycle accidents. The death rate for all causes of accidents and motorcycle accidents were 23.13 and 4.30 per 100,000 population, respectively. Seven models were considered as candidates. The SARIMA (0, 0, 0) (1, 1, 1)12 model was selected as the best model due to better fit and used to predict the number and trend of motorcycle accident deaths. Motorcycle accident deaths are predicted to decrease gradually in the next 36 months, from June 2021 to May 2024, affected by seasonal changes. Conclusion: The trend of death due to motorcycle accidents from 2006 to 2021 in East Azerbaijan was declining, and it is predicted to decrease slightly in the next three years as well. As this reduction may be attributed to many factors, it is recommended to investigate effective factors in future studies.

Analysis of the explosion risk of low-concentration oxygen-bearing coal bed methane in the low-temperature environment

In the process of deep cooling and liquefaction of low-concentration (concentration <30 %) oxygen-bearing coal bed methan, there is a risk of explosion because the methane concentration in the ultra-low temperature environment is within the explosion limit. It is of great guiding significance to study the occurrence mechanism and accident-causing process of such accidents. Firstly, the experimental system of low-temperature methane explosion characteristics is constructed, and it is found that when ignition energy exceeds a certain range, the process of deep cooling and liquefaction of low-concentration oxygen-bearing coal bed methane will cause explosion. Then, the occurrence of the gas explosion accident in low-temperature environment is briefly described, and the causes of the accident are analyzed preliminarily, and the dynamic characteristics of methane explosion propagation under ultra-low temperature conditions are studied by Fluent numerical simulation, and it is obtained that ultra-low temperature conditions have a promotion effect on the explosion pressure. Finally, based on the analysis of catastrophe theory, the traditional accident causation cusp catastrophe model is improved, and the “experimental safety state" and the “internal causes of explosion-external cause of explosion" are taken as the state variables and control variables respectively, and the explosion risk analysis model for deep cooling and liquefaction of low-concentration oxygen-bearing coal bed methane based on the cusp catastrophe theory is constructed. Based on the accident case, the process of accidents caused by risk factors is described, and the evolution mechanism of such disasters and accidents is clarified. Finally, the hazard state evaluation method for liquefaction explosion of low-concentration oxygen-bearing coal bed methane is proposed based on the catastrophe theory, and this evaluation model is applied to evaluate and analyze the hazard state of accident case. The evaluation results are consistent with the actual hazard state, confirming the rationality and feasibility of this evaluation method. In this paper, the key hazards of such accidents are found out in detail and provides support for safety decision-making in the deep cooling and liquefaction process of low-concentration oxygen-bearing coal bed methane.

Анализ процедурных компонентов расследования несчастных случаев на производстве, влияющих на эффективность системы управления охраной труда

Operations in the mining and metallurgical industry are associated with many types of occupational hazards. Traditionally, companies in the mining and metallurgical industry focus on ensuring occupational safety. The reduction of occupational injuries and providing safe working conditions are the main values of enterprises in the mining and metallurgical industry of the Russian Federation. When searching for efficient tools to ensure occupational safety, it is crucial to consider the tendencies of changing workflow and technical progress. There are many tools to reduce the level of occupational injuries; however, the efficiency of the process of investigation of industrial accidents conditions the prevention of similar accidents in the future. Accidents occurred to employees and other persons involved in the employer's production activities by performing their job duties or any jobs by the request of an employer as well as other lawful actions caused by employment relationships with the employer or committed in his/her interests, are subject to investigation and registration. In order to improve the occupational safety management system at a company, the study has analyzed the occupational injury rate in the Russian Federation, identified the dominating causes of industrial accidents, and the components of the investigation procedure of accidents affecting the efficiency of the process. The objective of the qualitative investigation of industrial accidents is the identification of deficiencies in business processes that caused the accidents as well as the development of corrective measures to eliminate the causes of accidents and prevent the recurrence of accidents with similar causes in the future.

Miner pose detection algorithm based on improved YOLOv7 in the coal mine

The unsafe behavior of workers in underground mines is a major cause of accidents. Recently, deep learning technology has become more and more widely used in the computer field and has achieved remarkable results, providing new opportunities for the research of miner behavior recognition algorithms. To address the current problem of large computation volume and low detection efficiency of multi-person posture detection in underground mines, we propose an improved algorithm that introduces the GSConv convolution module into the YOLOv7-Pose model and replaces the ELEN-W module with the GSELEN module to accelerate model convergence. We also introduce a group-convolution reconstruction SPPCSPC module to reduce parameter load and calculation amount and reduce the original 17 joint points to 14 joints to further realize a lightweight model. The experimental results showed that the improved YOLOv7-Pose model in this study decreased to 135.7 MB, by about 18.8%, while maintaining model accuracy. Therefore, this improvement can achieve a lightweight algorithm and improve real-time posture detection of construction workers in underground mines while ensuring model accuracy.

Перспективный метод повышения уровня профессиональных компетенций персонала в целях обеспечения безопасности производственных процессов в компании ОАО «РЖД»

The study considers the problems of the importance of improving the quality of professional development of employees and their competencies to prevent accidents at the production facilities of the company. Despite the implementation of modern technologies and innovations, the role of employees in workflows remains crucial. Accidents and incidents in RZhD JSC, including the events that occurred due to the fault of employees, have been analyzed. The causes of incidents and accidents at production have been identified. Proposals for the improvement of the process of personnel training have been formulated. A promising method in the form of an employee’s safety passport has been suggested as a solution. This means that each employee of the company must undergo continuous training during their entire professional career. To achieve high results, innovative solutions must be applied to the system of personnel training. The implementation of innovative methods will allow to control the workflow and take prompt measures to improve and develop the training system. The promising vision of the use of new engineering and technological solutions, digital options for occupational optimization and organization has been demonstrated.

Accidental Survey of Different Intersection of Junagadh City

Abstract: Intersections are known to be high-risk areas for traffic accidents due to the convergence of different streams of traffic. By conducting an accidental survey, identify patterns and common causes of accidents. This study attempts investigation and analysis to prevent accidents at intersection which have high number of accidents. To get an insight into the trend of road accidents we collect last 3 years of accident data of different intersection of Junagadh city from District Traffic Office of Junagadh by taking permission from SP office Junagadh. And selected intersection is “Majevadi gate” Because in last 3 years, maximum number of accidents have occurred at this intersection. To reduce accidents at this intersection, we design a rotary intersection at the Intersection.

Identifying Spatial Patterns of Road Accidents in Madaba City by Applying Getis-Ord Gi* Spatial Statistic

Road safety has become a subject of great interest among policymakers worldwide as they seek effective strategies to mitigate traffic accidents. There exist various approaches to examine the occurrences of road traffic accidents in terms of their spatial information and consequences. Geographical information systems (GIS) have been widely employed to analyse the spatial patterns of road traffic accidents; they offer various statistical analysis tools to reveal the hotspot locations of road accidents. Reducing the number of traffic accidents and overcoming their negative impact by defining the hotspot locations gain serious attention from the Public Security Directorate (PSD) in Jordan. This study analyses road traffic accidents in Madaba City using spatial statistics to determine the hotspot locations. The Gtis-Ord (Gi*) spatial statistics method was applied to 5730 reported traffic accidents between 2017 and 2019. The results accurately located the groups of chosen accidents and identified 37 hotspots, accounting for 1.89% of the reported cases. The Maximum Z score was 30.99, and 691 reported cases led to the identification of 13 high-priority hotspots. These hotspots occur at significant thoroughfares, busy roundabouts, and uncontrolled intersections. Driving errors and excessive speeding were the most common causes of fatal and non-fatal accidents in Madaba City. Efficient countermeasures to mitigate the number of accidents in Madaba City include adding more police inspectors to the city center, installing speed cameras, and putting up traffic signs at uncontrolled intersections. The outcomes of this work may encourage the PSD to adopt the GIS statistical tools in analyzing the spatial patterns of road traffic accidents to achieve more accurate results.

Analysis of Beijing Traffic Violations Based on the BERT-CRF Model

Traffic violations are a major cause of traffic accidents, yet current research falls short in comprehensively analysing these violations and the named entity method fails to extract the name of traffic violation events from records, thereby lacking in providing guidance for managing urban traffic violations. By expanding the People’s Daily dataset from 71,456 words to 95,291 words, the BERT-CRF (Bidirectional Encoder Representations from Transformers-Conditional Random Field) model achieves an accuracy rate of 88.53%, a recall rate of 92.90% and an F1 score of 90.66%, successfully identifying event, time and location named entities within traffic violations. The data of traffic violations is then enhanced through forward geocoding and the Bayesian formula, and traffic violations are analysed from time, space, administrative region, gender and weather, to provide support for the dynamic allocation of law enforcement forces on traffic scenes and the precise management oftraffic violations.