Causes Of Accidents Research Articles

AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation

Sea fog reduces visibility to less than 1 km and is a major cause of maritime accidents, particularly affecting the navigation of small fishing vessels as it forms when warm, moist air moves over cold water, making it difficult to predict. Traditional visibility measurement tools are costly and limited in their real-time monitoring capabilities, which has led to the development of video-based algorithms using cameras. This study introduces the Approximating and Eliminating the Airlight–Reduced DCP (AEA-RDCP) algorithm, designed to address the issue where sunlight reflections are mistakenly recognized as fog in existing video-based sea fog intensity measurement algorithms, thereby improving performance. The dataset used in the experiment is categorized into two types: one consisting of images unaffected by sunlight and another consisting of maritime images heavily influenced by sunlight. The AEA-RDCP algorithm enhances the previously researched RDCP algorithm by effectively eliminating the influence of atmospheric light, utilizing the initial stages of the Dark Channel Prior (DCP) process to generate the Dark Channel image. While the DCP algorithm is typically used for dehazing, this study employs it only to the point of generating the Dark Channel, reducing computational complexity. The generated image is then used to estimate visibility based on a threshold for fog density estimation, maintaining accuracy while reducing computational demands, thereby allowing for the real-time monitoring of sea conditions, enhancing maritime safety, and preventing accidents.

Analysis on typical characteristics and causes of coal mine gas explosion accidents in China.

Large-scale coal mine gas explosion (CMGE) accidents have occurred occasionally and exerted a devastating effect on society. Therefore, it is essential to systematically identify the characteristics and association rules of causes of CMGE accidents through analysis on large-scale CMGE accident reports. In this study, 298 large-scale CMGE accidents in China from 2000 to 2021 were taken as the data sample, and mathematical statistical methods were adopted to analyze their general characteristics, coupling cross characteristics, and characteristics of gas accumulation and ignition sources. Moreover, the text mining technology and the Apriori algorithm were used for exploring the formation mechanism of CMGE accidents, during which 46 main causal factors were identified and 59 strong association rules were obtained. Furthermore, an accident causation network was constructed based on the co-occurrence matrix. The key causal items and sets of CMGE accidents were clarified through network centrality analysis. According to the research results, electrical equipment failure, cable short circuit, mine lamp misfire, hot-line work, and blasting spark are the key ignition sources of CMGE. Fan failure, airflow short circuit, and local ventilation fan damage are the main causes of gas accumulation. Besides, the confidence levels of two association rules of "static spark-fan failure" and "blasting spark-airflow short circuit" are higher than 70%, indicating that they are the two dominant risk-coupling paths of gas explosions. In addition, six causes appear frequently in the shortest risk paths of gas explosion and are closely related to other causes, i.e., fan failure, local ventilation fan damage, static sparks, electrical equipment failure, self-heating ignition, and friction impact sparks. This study provides a new perspective on identifying causes of accidents and their complex association mechanisms from accident report data for practical guidance in risk assessment and accident prevention.

Research on coal mine safety risk evolution and key hidden dangers under the perspective of complex network

In order to find out the main causes of coal mine safety accidents and improve the pertinence of coal mine safety risk management and control, the identification and analysis of coal mine safety risks and hidden dangers are carried out based on the analysis of coal mine accident reports. Combing the complex network theory, a complex network model for the evolution of coal mine safety risks is constructed. The key elements that affect coal mine safety risk accidents are obtained through quantitative research on the characteristic indicators of the complex network model of coal mine safety risks. And the key nodes of coal mine safety risk spread network are obtained through network interference to the overall efficiency. The research results show that the complex network of coal mine safety risks illustrate the characteristics of a small-world network, and the spread of a certain risk is likely to cause coal mine safety accidents. Strengthening the risk management and control of hidden dangers with higher intermediate centrality can isolate the spread of coal mine safety risks and reduce the possibility of coal mine accidents.

INNOVATIVE INSULATING MATERIAL IN THE ASPECT OF ENSURING THE SAFETY OF HYDROCARBON TRANSPORTATION

Ensuring continuous and safe operation of the main oil and gas pipelines is a critical aspect for preserving the environment and efficient use of hydrocarbon resources. More than half of accidents on trunk gas pipelines occur due to corrosion wear of the metal caused by insufficient protection of the insulation coating. The consequences of such incidents have a devastating impact on the environment and human lives: spilled oil and oil products pollute the soil, and leaking gas can lead to explosions and forest fires.Corrosion processes hiding under the coating due to poor adhesion and due to non-fulfillment of their functions by the coating lead to increased peeling of insulation and the formation of new corrosion foci, including stress-corrosion processes. Consequently, imperfect insulation coating and insufficient adhesion to metal are one of the main causes of accidents on main pipelines.The purpose of this work was to study and prove the chemical interaction of the functional groups of asmol insulation compositions with the metal surface. In the course of work, the following tasks were solved:- assessment of the ratio of initial adhesion parameters obtained in laboratory and route conditions and their values after 1.5 and 6 years of operation;- study of the IR spectrum in order to prove the presence of functional groups in the asmol material;- study of the protective capacity of the asmol coating structure in an aggressive corrosive environment at different temperatures.Methods were used to measure the quantitative adhesion of the anti-corrosion coating and the peel area during cathode polarization, in accordance with State Standard R 51164-98. The composition of the asmol material was studied by IR spectroscopy.It has been shown that functional groups, the presence of which has been confirmed by spectral analysis, of asmol material in the insulation coating make it possible to chemically adhere to steel. Indirect proof of this is the stability of the adhesion strength over time, the cohesive separation of the asmol coating in determining adhesion indicators under trace conditions and the relatively small area of coating detachment during cathode polarization.

Vehicle Safety Application through the Integration of Flood Detection and Safe Overtaking in Vehicular Communication

Road safety in Malaysia is a major concern due to frequent floods and accidents caused by overtaking. These issues result in significant injuries and losses. In this paper, we introduce a new system called the Safe Driving Tool (SDT). The SDT integrates a Flood Detection System (FDS) and a Vehicle Overtaking System (VOS) using Long-Range (LoRa) communication technology. The FDS continuously monitors water levels in flood-prone areas. It alerts drivers about potential hazards through vehicle-to-infrastructure (V2I) communication. Simultaneously, the VOS enables safe overtaking maneuvers. It does this by exchanging information with nearby vehicles through vehicle-to-vehicle (V2V) communication. Through testing and experimentation, we have shown that the SDT system effectively reduces accident risks and losses associated with floods and overtaking. The system's performance under various conditions confirms the reliability and effectiveness of LoRa communication technology in enhancing vehicular safety. This study represents a significant advancement in road safety. It combines flood detection and overtaking assistance into a single unified system, addressing two major causes of road accidents in Malaysia. The integration of V2I and V2V communication provides a comprehensive solution that improves driver awareness and decision-making. This ultimately leads to safer driving environments and enhanced driver convenience. Doi: 10.28991/CEJ-2024-010-09-015 Full Text: PDF

Top causes of road traffic accidents in Romania

Abstract Road accidents worldwide impose a significant burden on national budgets, particularly affecting the least developed countries. According to the latest WHO “Global Status Report on Road Safety 2023,” road fatalities are the leading cause of death among young people aged 5 to 29. Among the European Union countries, Romania has the highest mortality rate. Between 2011 and 2021, approximately 2.8% to 1.1% of Romania’s GDP represented the social costs caused by road accidents. Statistical analysis of serious road accidents in Romania during this period reveals five main causes responsible for at least 50% of the total deaths recorded on the national public road network. The most significant cause is “speed not adapted to road conditions,” accounting for approximately 19% of the total. A road accident occurs due to the action or interaction of three determining factors: human, vehicle, and road (road environment). In most cases, the blame for road accidents is attributed to human error or behaviour. This conclusion is often influenced by data collection methods that do not consider all the influences of road infrastructure on the accident’s dynamics. A closer analysis shows that road infrastructure deficiencies often influence or cause road accidents, highlighting the importance of continuous evaluation and improvement of infrastructure to reduce accident risk. The implementation of Directive 2008/96/EC on road infrastructure safety management has prompted authorities to pay particular attention to infrastructure deficiencies in fatal accidents. During 2018-2021, following road safety inspections, auditors provided police and road administrators with detailed reports on deficiencies that caused or favored fatal accidents. Processing and analysing these reports can form the basis for developing a statistical model that provides a unified approach to road infrastructure deficiencies. This model can set up best practices for road safety, contributing to the uniform treatment of infrastructure problems found by auditors. With the transposition of Directive (EU) 2019/1936, which amends Directive 2008/96/EC, through Romanian Government Ordinance no. 3/2022, an innovative approach to managing road infrastructure deficiencies has been introduced. At the EU level, emphasis is placed on finding main road sections with low safety levels. Specific inspections are directed towards road sections with multiple risks, and remedial measures will focus on cost-effective solutions to enhance safety. Continuous evaluation and improvement of road infrastructure are crucial for reducing accident risk. By addressing deficiencies contributing to serious accidents, authorities can create safer road environments. This proactive approach involves detailed inspections and thorough data analysis to find and rectify potential hazards. The goal is to set up a system that not only responds to accidents but also prevents them by mitigating infrastructure risks. In conclusion, road safety management needs a multi-faceted approach that integrates human, vehicle, and road factors. By implementing directives and conducting rigorous inspections, authorities can significantly reduce road accidents. The development of a statistical model from inspection data will systematically address deficiencies, leading to safer roads and reduced fatalities, highlighting road safety as a public health issue.

Reimagining Reason’s Swiss Cheese Accident Causation Model

Reimagining Reason’s Swiss Cheese Accident Causation Model

Improvement Plan for Prevention of Safety Accidents in Old Facilities

Recently developed buildings are becoming larger, more complex, and higher-rise. After the announcement of the first new city in 1989, other first new cities were created in Bundang New Town in Seongnam-si, Gyeonggi-do, and Ilsan New Town in Goyang-si, where occupancy began in 1992. Subsequently, second new cities were built in 12 locations in 2003. In 2018, third new cities were announced, including the Wangsuk District in Namyangju-si, Gyosan District in Hanam-si, and Gyeyang District in Incheon. The continued announcement of new cities suggests the existence of old equipment in first and second new cities, necessitating the safety management of old facilities. Among major building facilities, cases of safety accidents have been reported due to aging, explosion of refrigerators, fires from aging water substation facilities, and power outages. In December 2018, an accident occurred in which one person was killed and 20 people were injured when a hot water pipe at the regional heating corporation near Baekseok Station in Ilsan ruptured because of aging. Thus, the aging of facilities is a major cause of safety accidents, and the scale of accidents is also increasing, making it urgent to establish improvement measures to counteract the occurrence of accidents in old facilities. To prevent safety accidents in old facilities, this study intends to contribute to safety accident prevention by presenting a replacement standard for the main facility through facility expert surveys and domestic and international useful prior research life management case studies.

Investigating occupational accidents based on the Human Factors Analysis and System Classification (HFACS) method: a case study in an industrial and mining company

Background and aims: Human errors are a significant issue in various work environments, including industry and mining. This study aims to investigate occupational accidents in a large industrial-mining company based on the human factors analysis and classification system (HFACS) method. Materials and methods: The study conducted a retrospective longitudinal analysis of 253 mining industrial accidents from 2011-2019 using the Human Factors Analysis and Classification System (HFACS). Descriptive statistics, such as frequency and percentage of subgroups at each level, were calculated using the HFACS method and SPSS software. Results: The type of accident that occurred was related to throwing/collision or contact with a foreign object, which resulted in a total of 232 injured people and one dead person. Statistical analysis showed no significant link between the accident type and the educational levels of those involved, as indicated by a P-value greater than 0.05.Still, it showed a significant relationship with three other demographic variables: age, marital status, and work experience (P-Value˂0.05). The highest frequency and percentage of the causes of accidents were also due to organizational effects and unsafe practices. Discussion and conclusion:. The study found that the frequency of human errors in the levels of unsafe supervision and unsafe practices significantly contributed to the occurrence of occupational accidents in the company. Analyzing accidents and understanding the relationship between causal factors at the four levels of the HFACS method is vital for implementing effective strategies to reduce accidents.

CO2 Breathing Prior to Simulated Diving Increases Decompression Sickness Risk in a Mouse Model: The Microbiota Trail Is Not Forgotten.

Decompression sickness (DCS) with neurological disorders is the leading cause of major diving accidents treated in hyperbaric chambers. Exposure to high levels of CO2 during diving is a safety concern for occupational groups at risk of DCS. However, the effects of prior exposure to CO2 have never been evaluated. The purpose of this study was to evaluate the effect of CO2 breathing prior to a provocative dive on the occurrence of DCS in mice. Fifty mice were exposed to a maximum CO2 concentration of 70 hPa, i.e., 7% at atmospheric pressure, for one hour at atmospheric pressure. Another 50 mice breathing air under similar conditions served as controls. In the AIR group (control), 22 out of 50 mice showed post-dive symptoms compared to 44 out of 50 in the CO2 group (p < 0.001). We found that CO2 breathing is associated with a decrease in body temperature in mice and that CO2 exposure dramatically increases the incidence of DCS (p < 0.001). More unexpectedly, it appears that the lower temperature of the animals even before exposure to the accident-prone protocol leads to an unfavorable prognosis (p = 0.046). This study also suggests that the composition of the microbiota may influence thermogenesis and thus accidentology. Depending on prior exposure, some of the bacterial genera identified in this work could be perceived as beneficial or pathogenic.

Robustness of higher‐order network pertinent to first‐ and higher‐order dependency

AbstractRobustness of higher‐order network (HON) with dependency is different from first‐order network (FON), as the attack orders and the number of iterations are totally different. To measure the robustness of HON with dependency, this study proposed new attack strategies on node and edge failure through measuring the size of the giant component and global efficiency. And two real‐world systems of hazardous materials transportation accident causation system and Chinese high‐speed railway system with different network size were empirically studied. Results show that HON is more fragile to robustness attacks than FON. The number of attack iterations is uncertain in HON, which depends on the attack strategies and the network structure. Meanwhile, nodes and edges with high dependency have greater impacts on network robustness, providing valuable insights into capturing critical information. This study may be helpful to design the robust system, improve the system's reliability and serve as a valuable reference for future exploration of HONs robustness.

Discussion on Fire and Explosion Prevention of Electrical Equipment

Electrical equipment fire and explosion-proof is to protect industrial production and daily life safety is an important task, in the context of the new era, the continuous development of science and technology, the types and number of electrical equipment is increasing, its fire and explosion-proof work is also facing new challenges. In this paper, on the basis of in-depth analysis of the causes of electrical equipment fire and explosion accidents, discusses the technical measures and management measures of electrical equipment fire and explosion prevention, in order to improve the safety of electrical equipment to provide reference.

Improving the Wettability and Fire Retardancy of Three-Phase Foam by Simulated Sea Mud

ABSTRACT As coal mining depths deepen and mining intensity increases, coal spontaneous combustion has become the leading cause of coal mining accidents. Sea mud is an environmentally friendly natural material rich in inorganic salts that inhibit coal oxidation. Based on sea mud, a new method of suppressing coal spontaneous combustion has been proposed by exploring the essential characteristics of sea mud three-phase foam with different concentrations. The results demonstrated that the contact angle of the three-phase foam solution with 20 wt% of sea mud decreased by 55.4° compared with that of pure water, the cross-point temperature of coal (245.85°C) increased by 16.15°C, and the peak temperature (490.57°C) increased by 16.47°C, which proved that its wettability and thermal stability were both enhanced. In conclusion, the three-phase foam with 20 wt% sea mud has been demonstrated to exhibit a superior ability to prevent the spontaneous combustion of coal.

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

Exploring the causes of frequent accidents at highway tunnel exits: Coupling analysis of the slack effect and white hole effect in extra-long tunnels

In this paper, the causes of frequent traffic accidents at the exits of highway tunnels are investigated. This study recruited 34 subjects to conduct vehicle experiments using an illumination meter, eye tracker, Smarter Eye and OBD to measure the illumination, eye movement characteristics, driving trajectory, and speed while driving in a tunnel exit. A questionnaire was used to investigate drivers’ subjective feelings while driving. The change trend of each indicator in the area near the tunnel exit was analyzed, and the relationships among the light environment, driver physiology, driving behavior, and driver psychology were explored. The psychological and physiological inconsistency phenomenon and abnormal acceleration behavior were observed at the tunnel exit, and the slack effect was verified. As the tunnel exit area is driven through, with the rapid increase in illumination, the rate of change of the driver’s pupil area increases, and the distance between the vehicle and the tunnel sidewall increases. It is unreasonable that in a tunnel exit with a poor traffic environment, some drivers experience psychological slack and tend to increase their speed and accelerate out of the tunnel. The slack effect is observed in most drivers, approximately 79.4% of the total. The white hole effect interferes with drivers’ vision and reduces their ability to obtain traffic information. The slack effect reduces drivers’ attention and increases vehicle speed. A coupling effect exists between the white hole and slack effects, which together threaten traffic safety at the tunnel exit.

Improved similarity analysis of industrial alarm flood sequences by considering alarm correlations

Alarm floods are leading issues that compromise the efficiency of industrial alarm systems and are identified as major causes of many industrial accidents. As an advanced technique to handle alarm floods, sequence alignment based similarity analysis has been developed to match alarm flood sequences, and thus can help with further root cause identification and early warning of alarm floods. However, existing methods based on biological sequence alignment algorithms ignore the relations between alarm occurrences, and thus may cause incorrect matches or mismatches of alarms when comparing two flood sequences. Accordingly, this paper proposes a new alarm flood similarity analysis method based on global vectors and Move–Split–Merge (MSM) distance. The contributions are mainly twofold: (1) An alarm encoding model based on modified global vectors is devised to convert alarm sequences into numerical vectors that reflect the correlations of alarms; (2) a similarity analysis method based on the modified MSM distance is proposed for comparison of encoded alarm flood sequences of unequal lengths. The effectiveness of the proposed method is demonstrated through a case study with a publicly accessible industrial model for Vinyl Acetate Monomer.

Hazard identification performance comparison between virtual reality and traditional construction safety training modes for different learning style individuals

The construction industry is one of the most dangerous industries worldwide, with many workers involved in accidents annually. The main cause of accidents is that hazards are not recognised, making safety training vital. Virtual reality (VR) technology offers a new approach to construction safety training. Therefore, exploring the hazard identification performance of individuals with different learning styles is what highlights our innovation in matching different training styles to different individuals. This study evaluates the performance of individuals using two training modes. Visual search eye-movement indicators were collected during hazard identification to explore their effects on hazard recognition performance. Simultaneously, learning style theory was introduced to classify individuals using eye-tracking technology and then investigate the differences in hazard identification performance enhancement of individuals with different learning styles after different safety training. The results showed that VR safety training improves individual hazard identification performance more than traditional safety training, but VR safety training is not necessarily suitable for everyone. Reflective, verbal, and global learners can achieve greater improvement by participating in traditional safety training; active, intuitive, and sequential learners can achieve greater improvement through VR safety training; and sensing and visual learners do not exhibit a significant difference in improvement by participating in the two training modalities. Therefore, sensing and visual learners can choose to participating in traditional safety training, which is less costly and easier. This study provides meaningful recommendations for personalised construction safety training.

Seafarer competency analysis: Data-driven model in restricted waters using Bayesian networks

Despite the efforts of maritime authorities to enhance seafarer competencies through the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), human error remains a leading cause of maritime accidents. To thoroughly investigate the impact of various human errors among seafarers on accidents, this paper aims to examine the relationships between seafarer competencies and maritime accidents using a data-driven approach from the perspective of bridge resource management (BRM). Through analysis of historical maritime accident reports, the dataset of seafarer competencies associated with maritime accidents is established. The least absolute shrinkage and selection operator (LASSO) method is employed to identify the critical seafarer competencies for accident prevention. Then, a data-driven Bayesian Network (BN) model, based on a Tree Augmented Naive Bayes (TAN) method, is constructed to reveal the relationship between seafarer competencies and accident types, which are validated by sensitivity analysis and case study. The results indicate that the key seafarer competencies for all maritime accidents are ‘Maneuvers’, ‘Amend/maintain ship course’, ‘Decision making’, ‘Cognitive capacity’, ‘Information’, ‘Procedure operations’, ‘Situational awareness’ and ‘Communication’. Moreover, the study underscores the importance of leveraging lessons learned from past accidents to mitigate risks and ensure safe maritime operations. The findings contribute to a deeper understanding of the dynamics between seafarer competencies and accident types, unveiling the joint impact of different seafarer competencies on maritime accidents. This perspective offers valuable insights for maritime authorities in strengthening maritime safety regulations.

Frequency of Injuries and Deaths Due to Traffic Accidents Caused by the Driver, Bishkek

Road traffic injuries due to the severity of their adverse consequences are one of the priority health problems both in the Kyrgyz Republic and around the world. In Bishkek, the most common causes of traffic accidents caused by the driver were violations of the rules of maneuvering, violation of the rules for the passage of a pedestrian, crossing, non-observance of the rules of priority of travel, road signs, signals, speeding, driving into the oncoming lane, double overtaking, driving while intoxicated, non-observance of distance. However, it should be taken into account that for reasons such as violation of maneuvering rules (2.5 times), violations of the rules of passage of a pedestrian, crossing (2.9 times), non-compliance with the rules of priority of passage (1.6), disobedience to road signs, signals (2.9 times), driving into the oncoming lane, double overtaking (1.8 times), drunk driving (1.7 times), non-compliance with the distance (3.0 times), violation of the rules for passing inspection, use, reception (3.2 times), the frequency of injuries increased. In general, in Bishkek, for the period from 2016 to 2022, 549 people died from road accidents, of which 418 (76.1%) were caused by the driver of passenger vehicles. The highest proportion, both in general and through the fault of the driver of passenger vehicles, was made up of those who died from traffic accidents due to violation of maneuvering rules, violation of pedestrian, crossing, speeding.

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.