Accident Factors Research Articles

Establishment of Safety Management Measures for Major Construction Workers through the Association Rule Mining Analysis of the Data on Construction Accidents in Korea

Despite increasing industrial advancements, fatal and severe accidents, such as “Falls”, “Struck-by”, “Hit by an object”, “Be crushed”, and “Caught-in/between” accidents, persist in developed countries, including Korea. Various methods, including risk assessment, monitoring systems, technology improvements, and safety education, are being implemented to reduce accidents. However, only a few studies have revealed the causes of accidents and their interrelationships; these studies are based on limited data. Korea recently published accident data using national statistical systems, including the construction safety management integrated information (CSI), enabling the analyses of major accident types. Here, we selected various representative accident cases to minimize the duplication of the data published from 2019 to 2023 and applied the Material, Method, Machine, or Man (4M) analysis method, a risk assessment technique, to perform an accident-type-based association rule mining (ARM) analysis of the accident factors. Through the ARM analysis, we quantitatively identified complex correlations for major accidents. Based on the 4M factors derived through this analysis, we improved a 2–4 model for accident causation and proposed safety management measures for each construction entity.

A Physics-Based Methodology to Improve the Mitigation of Loss of Tail Rotor Effectiveness

Loss of tail rotor effectiveness (LTE), also known as unanticipated yaw, has been recognized to be a major contributing factor in several helicopter accidents due to the loss of directional control. Flight tests have identified specific wind azimuth regions that may alter the angle and the speed of the airflow through the tail rotor leading to LTE. Through the helicopter flight data monitoring (HFDM) program, pilots receive constant flight evaluation reports to support LTE risk mitigation. Nevertheless, the existing LTE safety metric presents several pitfalls that hinder the reliability of the detection of violations of boundaries for flight safety. To identify the appropriate methods to properly simulate and flag LTE events and develop a more comprehensive and reliable LTE safety metric to be used within the HFDM environment, a physics-based investigation of the different LTE flight characteristics is performed. The results from this investigation are expected to contribute towards an improvement in detection of LTE events, leading to a reduction in LTE-related accidents.

Real-time risk assessment of multi-parameter induced fall accidents at construction sites

In this research, a risk assessment model was developed that concurrently considers the location and physiological risk factors for fall accidents in workers at construction sites. In terms of location risk, data on 1683 occupational fall accidents involving 1718 worker casualties in the construction industry were collected for the period 1996–2016 from the Institute of Labor, Occupational Safety and Health (ILOSH), Taiwan. Next, the locations of these fall accidents were identified and defined as hazardous areas, with each area assigned a fall-risk level based on the average number of occupational accidents per year. In terms of physiological risk, the thermal hazard evaluation model, Recommended Alert Limits/Recommended Exposure Limits (RALs/RELs), developed by the National Institute for Occupational Safety and Health (NIOSH), USA was used in this study. This model uses metabolic heat and wet bulb globe temperature (WBGT) to recommend hourly rest/work times for workers. Using WBGT and one hour as the basic unit of time, varying degrees of metabolic heat, defined as metabolic heat loading severity (MHLS), were obtained. An optical heart rate (HR) sensor was applied in this study to measure HR, which was subsequently converted to a metabolic heat value using the Malchaire J metabolic heat evaluation model. Next, metabolic heat loading risk level was evaluated based on the MHLS. Finally, the location-based fall-risk level was multiplied by the MHLS, generating a risk evaluation model for fall accidents. The evaluation results identify the real-time fall-risk level of each worker and, if needed, suggest actions to be taken to mitigate the elevated risks of fall accidents identified by the model.

Risk Analysis of Work Accidents in the Construction Project of Class D Pratama Hospital Building Construction of Nias Regency

In the construction project of RSU D Pratama in Nias Regency, this study aims to identify variables that increase the risk of work accidents. The author of this study used quantitative methods. The aspects used by researchers to identify the risk of work accidents include Factors of Work Accidents, Factors Causing Disease, Factors Causing Work Accident Hazards in Development Construction Projects, and Protection Factors for the Application of Occupational Safety and Health. Based on the results of the study, almost all aspects of the Class D Primary Hospital building in Nias Regency have occupational accident risks that have an impact on Occupational Safety and Health (K3).

Identification of the best machine learning model for the prediction of driver injury severity

Predicting the injury severities sustained by drivers engaged in road traffic accidents is a key topic of research in road traffic safety. The current study analyzed the driver injury severity (DIS) using twelve machine learning (ML) algorithms. These models were implemented using 0.70, 0.80, and 0.90 train ratios and 5-, 10- and 15-fold cross-validation. Ten years of accident data (from 2011 to 2020) was obtained from police department of Shillong, India. A total of 693 accidents were documented, with 68% being nonfatal and 32% being fatal. Precision, recall, accuracy, F1 score and area under the curve measures were used to compare the performance of all twelve ML models. Overall, the light gradient-boosting machine model was shown to be the best ML model for predicting the injury severities of drivers engaged in road traffic incidents. Finally, variable importance analysis results showed that cause of accident, collision type and types of vehicles were the most influencing factors in nonfatal and fatal driver accidents. The results also revealed that age and gender were slightly associated with DIS. The findings of the current research could be helpful to road safety agencies for the implementation of suitable countermeasures to increase driver safety in road accidents.

Interaction mechanisms of interface management risks in complex systems of high-speed rail construction projects: an association rule mining-based modeling framework

PurposeThis study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.Design/methodology/approachThis paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.FindingsThe results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.Originality/valueThe interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

A Mini Review (PRISMA) on Causes of Incidents and Injuries Occurring in Agricultural Workplaces

Agriculture, alongside construction and mining, is one of the three most hazardous industries, and is characterized by numerous risk factors for occupational accidents. Unlike other industries, agriculture faces significant safety concerns related to the natural environment. Determining the causes of accidents is therefore imperative for accident prevention. However, the difficulty in investigating accidents owing to inadequate reporting and management systems among self-employed farmers hampers the determination of their causes. This study aims to determine the factors influencing agricultural accidents through a literature review. A systematic literature search was conducted according to the PRISMA guidelines. The initial search yielded 165 articles of which 34 papers were selected after removing those by applying the selection criteria. The selected papers were categorized into three domains: work accidents, machinery accidents, and farmer safety promotion. Accident causes were classified using the 4M technique: machine, media, man, and management. The results were visualized with a heat map. The main causes of accidents thus identified were insufficient operation/management, inappropriate work situation, and insufficient education/training. The findings of this study can serve as foundational data for developing measures to improve working environments not only in agriculture but also in other high-hazard industries.

CORRELATION ROAD GEOMETRIC ON ROAD DAMAGE

The implementation of safe road infrastructure can be defined by the minimum number of traffic accidents that occur on these roads. Traffic accidents can caused by several factors, encompassing the geometric attributes of the road, characteristics of vehicles, human elements, and environmental conditions. Previous studies have identified specific geometric features, including superelevation, degree of curvature, road slope, and shoulder type, as influential factors in traffic accidents. Simultaneously, the state of the pavement, characterized by issues such as rutting, potholes, IRI value, and skid resistance, also plays a crucial role in traffic accident. The interplay between road geometric and pavement conditions underscores the importance of effective road management for ensuring safety. Existing research highlights a correlation between road geometric and road damage, with the Pavement Condition Index (PCI) directly correlating with road and shoulder width, and inversely correlating with transverse and longitudinal road slope. This phenomenon is, in part, attributed to heightened shear strain on narrow road shoulders and a reduction in the resilience modulus value of the subgrade on sloped roads.

Identifying excessive chronic alcohol use with phosphatidylethanol in patients with suspected severe injury-results from the IDART study.

Acute and chronic alcohol use are well-known risk factors for accidents and injuries, and concurrent psychoactive drug use can increase injury risk further. Phosphatidylethanol (PEth) 16:0/18:1 is a biomarker used to determine alcohol consumption the previous 3-4 weeks. The aim was to investigate the prevalence of chronic alcohol use in trauma patients, as determined by PEth 16:0/18:1 concentrations, and how excessive chronic alcohol use relate to demographic variables, injury mechanisms and drug use. Patients received at Norwegian trauma hospitals from March 2019 to February 2020. The study is part of the Impairing Drugs and Alcohol as Risk factors for Traumatic Injuries study. All patients aged ≥ 16 years received with trauma team were included in the study. Data on injury date and mechanism, gender and age was registered. Blood samples were analyzed for 22 psychoactive medicinal and illicit drugs, ethanol and phosphatidylethanol 16:0/18:1. Regression analyses were conducted to assess associations between alcohol use and gender, age, injury mechanism and drug use. Of the 4845 patients included in the study, 10% had PEth 16:0/18:1 concentration ≥ 600 nM (~430 ng/mL), indicative of excessive chronic alcohol use. Being male, between 44-61 years old, involved in violence, and testing positive for medicinal drugs was associated with excessive chronic alcohol use.Excessive chronic alcohol use was common among males, middle-aged, patients with violence as injury mechanism and those with medicinal drug use. These findings emphasize the need to detect and treat excessive chronic alcohol use among trauma patients.

Unraveling the relation between cycling accidents and built environment typologies: Capturing spatial heterogeneity through a latent class discrete outcome model

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to many hazardous circumstances or environments, resulting in accidents, injuries, and even death. Transport authorities must understand why accidents occur, to reduce the risk of those who cycle.This study applies a new modeling framework to analyze cycling accident severities. We employ a latent class discrete outcome model, where classes are derived from a Gaussian–Bernoulli mixture, applied to data from Berlin, and augmented with volunteered geographic information. We jointly estimate model components, combining machine learning and econometric approaches, allowing for more intricate and flexible representations while maintaining interpretability.Results show the potential of our approach. Risk factors are indexed depending on where accidents occurred and their contribution. We can discover complex relations between specific built environments and accident characteristics and uncover differences in the impact of certain accident factors on one environment typology but not others. Using multiple data sources also proves helpful as an additional layer of knowledge, providing unique value to understand and model cycling accidents. Another critical aspect of our approach is the potential for simulation, where locations can be examined through simulated accident features to understand the inherent risk of various locations.These findings highlight the ability to capture heterogeneity in accidents and their relation to the built environment. Capturing such relations allows for more direct countermeasures to risky situations or policies to be designed, simulated, and targeted.

Exploring human factors of major chemical accidents in China: Evidence from 160 accidents during 2011–2022

Major chemical accidents (MCAs) usually cause multiple casualties, property losses, and environmental damage. MCAs in hazardous chemical production enterprises (HCPEs) in China have been frequent in recent years. Understanding past accidents is essential for accident prevention and risk mitigation. However, no accurate statistical analysis and systematic human cause investigation of MCAs in China is available. Hence, this study systematically identified the main characteristics and human factors of MCAs in China during 2011–2022 using official accident investigation reports. First, the 160 MCAs were investigated by mathematical and statistical methods involving eight main characteristics: evolution trend, spatial locations, accident types, domino effects involved, occurrence links, time volatility, hazardous chemicals involved, and enterprise scales. Second, based on the characteristics of MCAs and existing research on chemical accidents, the Human Factors Analysis and Classification System (HFACS) for Chemical Accidents (HFACS-CA), which consists of 26 cause categories and is suitable for the analysis of chemical accidents, was proposed on the basis of HFACS and other extended HFACS. Then, the selected 146 representative MCAs were analyzed using the HFACS-CA framework; the human factors (comprising human and organizational factors (HOFs) and outside factors) of MCAs in China were systematically explored. Finally, according to the main characteristics and human factors of MCAs, comprehensive recommendations for the prevention of MCAs involving enterprises, governments, and interested parties were proposed. This work can provide a useful human factor analysis tool for investigating chemical accidents and valuable information for preventing MCAs in China and other countries.

Personality traits and traffic violations: the serial mediating role of theory of planned behavior constructs and intention

Personality traits and traffic violations: the serial mediating role of theory of planned behavior constructs and intention

Using Multilayer Perceptron Neural Network to Assess the Critical Factors of Traffic Accidents

This study is based on the traffic accident data of Taoyuan City from the government's open data. The study compiled the data set of traffic accidents in Taiwan from 2012 to 2017, and six classifiers were applied to evaluate the effectiveness of traffic accident prediction with the number of injuries as the prediction target. In order to verify the classifier's stability, cross-validation was used to evaluate the model during the training process, and the multilayer perceptron neural network (MLPNN) classifier performed best in testing the dataset's accuracy and evaluating the model's best performance. Then, a boosting ensemble learning approach and a combination of traffic accident factors improve the experiment's performance. According to this experiment, the results show that this study uses the Pearson Chi-square feature selection method to select important traffic factor combinations, and the boosting method indeed helps improve the effectiveness of the construction of the traffic accident model. Finally, the experimental results of the NN-MLP model have a correct rate of 77% and AUC is 78.7%. In constructing the model, it was found that the degree of injury, the part of the vehicle hit, the type of accident, the leading cause, the type of vehicle, and the period of the accident were the main factors causing dangerous traffic accidents. Doi: 10.28991/HIJ-2024-05-01-012 Full Text: PDF

Study on the method of evaluating the failure of stator bar insulation materials based on mechanical damage characteristics

The damage impact of mechanical shocks on stator bar insulation is a key factor in high-voltage motor accidents. This study conducted electrical aging acceleration and breakdown experiments on damaged stator insulating materials to understand failure causes and evaluation methods. The relationship between sample defect area and depth and electrical properties was analyzed, along with the morphology of the damaged surface using a scanning electron microscope. Results show that the defect area correlates with impact mode but does not directly impact electrical performance. Effective insulation thickness is positively linked to electrical performance. This study serves as a valuable reference for evaluating mechanical impact faults in high-voltage motors.

Differences in Perceptions Regarding the Causes of Accidents in Logistics Vehicles: Freight vs. Passenger Cars

What factors do truck drivers consider as the primary causes of accidents? Study 1 investigated whether there were differences in risk perception among drivers of freight and passenger cars regarding various accident causes. Truck drivers perceived lower accident risks due to external factors during longer distances compared to shorter distances. Conversely, passenger car drivers felt lower risks at closer distances than at longer ones. This discrepancy can be attributed to variations in risk perception for the same accident factor, influenced by psychological fluency and the fit of regulatory focus with psychological distance.
 Study 2 explored conditions where the interaction effect between driving distance and regulatory focus influenced by driver type (freight vs. passenger car), differed from Study 1. Participants indicated that, when anticipating driving long distances while intoxicated, the perceived risk of drunk driving decreased if they were identified as other truck drivers. However, when identifying themselves as truck drivers, the risk perception of drunk driving increased significantly. This pattern was also replicated in the context of drowsy driving.

Analysis of Sedation Accident Records Available in the Japan Council for Quality Health Care Public Database.

Medical accidents occur frequently. However, only a few studies have been conducted on sedation-related medical accidents. This study aimed to classify sedation accidents and analyze their causes using the (Patient-management Software Hardware Environment Livewear (P-mSHELL) model. The Japan Council for Quality Health Care database on medical accidents was used. Sedation accidents that occurred during procedures between January 2010 and June 2022 were included. After examining the accident details for several variables, the accident factors were classified by factors in the P-mSHELL model, and statistical analyses, including multivariate analysis of each factor and outcome (presence or absence of residual disability), were conducted. Regarding the influence of the P-mSHELL factors on outcome, P factor (odds ratio = 6.347, 95% confidence interval = 2.000, 20.144) was a factor for having disability. There was a significant association between outcome and accident timing (that is, preoperative, intraoperative, or postoperative; p =0.01). No significant association was found between the outcomes and types of drugs used (p =1, 0.722, 0.594); however, there was a significant association between the incidence of respiratory depression and multiple drug use (p <0.001). To prevent sedation accidents, it is important to monitor patients throughout the perioperative period. However, it is especially important to know the patient's condition in advance, and strict postoperative management is essential, especially for high-risk patients, to prevent serious accidents.

CHALLENGES AND OPPORTUNITIES FOR IMPROVING MANAGEMENT AND REGULATION OF THE RAIL TRANSPORT SECTOR IN INDONESIA: PERSPECTIVE FROM THE INDUSTRY 4.0 CONCEPT

Objective: The research aims to uncover the causes of train accidents in Indonesia beyond those identified by experts and proposes solutions for inclusion in the regulations and standard operating procedures of the Indonesian Railways Company. Theoretical Framework: The United Nations Economic Commission for Europe (UNECE), operating under the UN Economic and Social Council since its establishment in 1947, has introduced the Industry 4.0 concept, titled 'Increasing the Competitiveness of Transportation between Europe and Asia: European Union Rail R&amp;I. This concept incorporates three crucial indicators known as ACT (Aging, Climate Change, and Technology), aligned with the Railway Sustainable Development Goals. Method: This research employs a qualitative approach, gathering data through in-depth interviews using purposive sampling. Interview reviews are presented in the results section. The study involved 28 participants, focusing on the cities of Palembang and Lampung, situated on the tip of the island of Sumatra, bordering the island of Java. Results and Conclusions: The study identified seven themes for inclusion: The development of Railways in Indonesia; Organizational strengths and weaknesses; Human resources; Infrastructure; Operational aspects; Causal factors in train accidents, and Future solutions and contributions to regulation. These were then simplified into three themes following UNECE guidelines. Notably, the main issues with freight trains, primarily used for coal transportation, include an inadequate number of carriages, damage to rails and train sleepers, and excessive cargo loads, leading to frequent train derailments and accidents. The law regulates general provisions for rail freight transportation, but violations are common, and sanctions are minimal due to the state-owned nature of the managing company. Research Implications: This study suggests measures to enhance regulations and Standard Operating Procedures (SOPs) for the Indonesian Railways Company, including adapting regulations and SOPs to improve the operational safety of goods trains. This involves modernizing signaling systems and addressing human errors, particularly during night shifts. Recommendations also cover optimizing single-path and double-path routes, proposing regulations for the adaptation of automatic control systems, and improving the Grade of Automation (GOA). Ensuring the safety of personnel, especially operators and machinists, is emphasized. Originality/Value: This research explores the paradigm shift within Indonesia Railways, transitioning from GOA 0 to GOA 1. Anticipated to significantly impact the safety of goods (coal) trains, this study offers profound insight into the evolution of transportation governance for freight transport by rail. Both the government and Indonesia Railways can consider integrating these findings into regulations or SOPs, despite facing challenges such as funding constraints and the high cost associated with upgrading to GOA 2 infrastructure.

Effect of network topologies and attacking strategies on cascading failure model with power-law load redistribution

Various traffic networks play an important role in daily life and have different topological characteristics such as small-world and scale-free. The factors of traffic congestion, natural disasters and traffic accidents may induce cascading failure in which the load redistribution usually has the characteristic of power-law (that is to say, when a station is broken, the great majority of passengers would evacuate to the nearby stations, but the very few would evacuate to further). Based on them, this paper studies the load-capacity cascading failure model with power-law load redistribution using three kinds of attacking strategies (removing a single node according to the load, removing multiple nodes according to the load and removing multiple nodes randomly) for different network structures (random networks, WS small-world networks and BA scale-free networks). We confirm the robustness of our results by further performing simulations on real-world traffic networks. A quantitative description of network topologies and attacking strategies vs. the cascading failure model with power-law load redistribution is obtained by computer simulation. The results demonstrate that in contrast to removing the node according to the load, network robustness increases with the increase of the load control parameter α when randomly removing the node; when θ is small, the load tolerance parameter β is sensitive to the network robustness in random networks and WS small-world networks when removing a single node according to the load.

Advancements in Motorcycle Safety: Automatic Helmet Detection Systems Through Machine Learning

Motorcycle accidents are a huge concern today. The number of accidents occurring every day has increased rapidly which in turn results in loss of lives. One of the main factors in these fatal accidents is the lack of use of helmets. Even though the use of a helmet is mandatory people fail to follow it which causes these unfortunate events. The government has taken various initiatives regarding the increase in the use of helmets by creating awareness and also increasing the fines but people seem to be least bothered. To tackle these situations, we propose an automated solution for helmet detection on motorcyclists using a combination of techniques like YOLO and CNN. The combination of You Only Look Once (YOLO) and Convolutional Neural Network (CNN) helps to efficiently identify the helmets from the real-time datasets. The proposed system will help traffic police by reducing their work and automating the imposition of fines. As the techniques are used in combination, the accuracy and the degree of reliability of our system are higher compared to single techniques-based implementations. Key Words: Open CV-Open-Source Computer Vision Library ,CNN-Convolutional Neural Network ,OCR-Optical Character Recognition ,YOLO-You Only Look Once ,IOU-Intersection over Union ,DHCP-Dynamic Host Configuration Protocol ,DDNS-Dynamic Domain Name System ,PoE-Power over Ethernet ,API-Application Programming Interface.

INVESTIGATING THE RISK FACTORS AFFECTING THE OCCURRENCE, FREQUENCY, AND SEVERITY OF LARGE TRUCK ACCIDENTS IN AL-NAJAF GOVERNORATE, IRAQ

In spite of the established literature-based evidence regarding the consequences of large truck accidents, limited body of research has been done on the characteristics and risk factors of such road accidents in Iraqi cities and governorates. According to national statistics, there has been a steady increase in the number of trucks and truck-related crashes over the past ten years. This paper aims to investigate the characteristics and risk factors associated with accidents involving large trucks in Al-Najaf governorate. A sample of 400 truck drivers were randomly selected and interviewed to collect the needed accident data. Four generalized linear models have been built; ordinal regression model for total injuries, binary logistic model for fatal accident occurrence, multinomial logit model for accident frequency, and ordinal regression model for accident cost. The analysis results revealed several influential predictors including truck driver age, education level, type of collision, truck speed, truck type, and street lighting condition. The obtained findings should be enlightening and helpful for government organizations looking to promote safety measures for sustainable freight truck transport.