Workplace Accidents Research Articles

Application of Machine Learning Modeling for the Upstream Oil and Gas Industry Injury Rate Prediction

Introduction: Yearly, the International Labor Organization report indicates many workplace accident occurrences. The degree of the happenings depends on the workplace environment setting and the incident regulatory measures implemented. By the nature of its work environment, the oil and gas upstream sector is susceptible to high incident rates. In the current fierce business competition and practices, improving productivity, quality, and other processes, such as Safety, is vital. Implementing well-designed safety procedures is the key to managing and reducing the risk level of workplace incidents. Methods: Recently, the application of Machine learning (ML) modeling for accident/injury prediction has been reported in the construction, mining, transport, and health sectors. Likewise, the objective of this paper was to implement three machine-learning-based models to predict injury rates in a drilling operation. The petroleum safety authority of Norway provided the datasets. First, the dataset was pre-processed, and then the selected features and target dataset were used for the modeling. Finally, the model prediction and performance accuracy analysis were performed. Results: Results showed that multivariable regression (MVR), Random Forest (RF), and Artificial Neural Network (ANN) machine learning algorithms-based models predict the test data with R2 values of 0.9576, 0.793, and 0.97036, respectively. Conclusion: As the common saying goes, 'prevention is better than cure.' For this, implementing methods such as improved work processes and Health, Safety, and Environment (HSE) mitigation procedures, workplace injuries, and accidents allow for reducing the risk level of workplace injuries. The application of integrated machine learning tools, along with carefully built-in workplace accident database implementation, will provide early detection and possible remedial precautions that can be taken to prevent workplace injuries/accidents/fatalities. However, extensive research and development are required to deploy the method in real life. Combining Machine Learning modeling and carefully designed safety measures is vital for successful and robust predictive tools.

Pioneering Maintenance Strategies for Enhanced Reliability of Low Voltage Switchboards

This study highlights the critical need for rigorous maintenance protocols, focusing on routine inspections, preventative measures, and timely corrective actions. Low voltage switch panels (PHB-TR) are vital for ensuring the efficiency and reliability of electricity distribution systems. Through systematic analysis of existing maintenance procedures and field evaluations across diverse setups, the research confirms that structured maintenance not only mitigates the risk of failures but also significantly reduces workplace accidents. The findings advocate for strict adherence to standard operating procedures and improved coordination among technicians, suggesting these strategies as global standards for enhancing the safety and functionality of electrical distribution networks. Highlights: Proper maintenance ensures optimal performance and reduces the risk of system failure. Adherence to applicable SOPs is crucial to prevent work accidents. Coordination and adherence to job distribution streamline work execution. Keywords: PHB-TR Maintenance, Split Switch Panel Maintenance, Low Voltage Split Switch Board

Unsafe Conditions Identification Using Social Networks in Power Plant Safety Reports

Power plants in Indonesia grapple with significant challenges in managing occupational health and safety. Power generation companies urgently need to reduce workplace accidents every year and need an application for reporting every potential workplace hazard. The huge reporting data in applications such as IZAT requires thorough analysis to find out the pattern and distribution. This research aims to facilitate the company in hazard mitigation by identifying reported unsafe conditions and building a semantic association network to understand the nature of unsafe conditions between Paiton and Indramayu generating units. The research method uses social network analysis, which is carried out by preprocessing the data using programming to remove noise and then converting the data into a readable format. Then, semantic relationships between words were analyzed, and the data was visualized using the ForceAtlas2 algorithm. The findings revealed a different focus between the two units, where 6.597 reports from the Paiton generating unit mainly highlighted team response and accident-prone workplace conditions, while 5.840 reports from the Indramayu unit emphasized specific conditions, locations, and equipment that pose accident risks

Deep Learning for Detection of Proper Utilization and Adequacy of Personal Protective Equipment in Manufacturing Teaching Laboratories

Occupational sectors are perennially challenged by the potential for workplace accidents, particularly in roles involving tools and machinery. A notable cause of such accidents is the inadequate use of Personal Protective Equipment (PPE), essential in preventing injuries and illnesses. This risk is not confined to workplaces alone but extends to educational settings with practical activities, like manufacturing teaching laboratories in universities. Current methods for monitoring and ensuring proper PPE usage especially in the laboratories are limited, lacking in real-time and accurate detection capabilities. This study addresses this gap by developing a visual-based, deep learning system specifically tailored for assessing PPE usage in manufacturing teaching laboratories. The method of choice for object detection in this study is You Only Look Once (YOLO) algorithms, encompassing YOLOv4, YOLOv5, and YOLOv6. YOLO processes images in a single pass through its architecture, in which its efficiency allows for real-time detection. The novel contribution of this study lies in its computer vision models, adept at not only detecting compliance but also assessing adequacy of PPE usage. The result indicates that the proposed computer vision models achieve high accuracy for detection of PPE usage compliance and adequacy with a mAP value of 0.757 and an F1-score of 0.744, obtained with the YOLOv5 model. The implementation of a deep learning system for PPE compliance in manufacturing teaching laboratories could markedly improve safety, preventing accidents and injuries through real-time compliance monitoring. Its effectiveness and adaptability could set a precedent for safety protocols in various educational settings, fostering a wider culture of safety and compliance.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach

Introduction: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts’ subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. Method: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. Conclusions and Practical Applications: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Implementing Circular Economy Principles to Enhance Safety and Environmental Sustainability in Manufacturing

The global manufacturing sector is confronted with mounting challenges related to safety hazards and environmental sustainability. In response, there has been a growing emphasis on adopting circular economy principles as a transformative approach to address these pressing issues. This review explores the implementation of circular economy principles within manufacturing processes to enhance safety and environmental sustainability. Circular economy principles prioritize resource efficiency, waste reduction, and the continual use of materials within closed loops. By shifting from the traditional linear "take-make-dispose" model to a circular approach, manufacturing facilities can significantly mitigate safety risks associated with hazardous waste generation and disposal. Moreover, the adoption of circularity fosters a culture of preventive measures, reducing the likelihood of workplace accidents and improving overall safety standards. Furthermore, integrating circular economy principles into manufacturing operations contributes to environmental sustainability by minimizing resource depletion and pollution. Through strategies such as product redesign for recyclability, remanufacturing, and waste valorization, manufacturers can reduce their reliance on virgin materials and curb emissions. This transition towards circularity not only diminishes the environmental footprint of manufacturing activities but also creates opportunities for cost savings and innovation. However, the successful implementation of circular economy principles in manufacturing requires a concerted effort from stakeholders across the value chain. Collaboration among manufacturers, suppliers, policymakers, and consumers is essential to overcome barriers such as technological limitations, regulatory constraints, and behavioral change resistance. Embracing circular economy principles holds immense potential for enhancing safety and environmental sustainability in manufacturing. Through systematic integration and collaboration, the manufacturing sector can pave the way towards a safer, more sustainable future.

Assessing the environmental health and safety risks of solar energy production

Solar energy production has gained significant traction as a promising alternative to fossil fuels, yet its widespread adoption raises questions regarding its environmental health and safety (EHS) risks. This review presents an overview of the current state of research in assessing these risks associated with solar energy production. Firstly, it examines the environmental impacts of solar energy, including the life cycle assessment of photovoltaic (PV) panels and solar thermal systems. Key considerations include the energy and resources required for manufacturing, transportation, installation, operation, and end-of-life disposal or recycling of solar panels. Furthermore, the potential for land use change, habitat disruption, and biodiversity loss due to large-scale solar installations is addressed. Secondly, the review discusses the safety risks associated with solar energy production, focusing on occupational health and safety hazards for workers involved in manufacturing, installation, maintenance, and decommissioning of solar energy systems. It examines exposure to hazardous materials such as lead, cadmium, and silicon during the manufacturing process, as well as the risks of falls, electrical hazards, and other workplace accidents during installation and maintenance activities. Moreover, the review highlights emerging technologies and best practices aimed at mitigating EHS risks in solar energy production. These include advancements in PV panel recycling technologies, improvements in manufacturing processes to reduce environmental impacts, and enhanced safety protocols and training for workers in the solar energy industry. While solar energy offers numerous environmental and economic benefits as a renewable energy source, it is essential to comprehensively assess and manage its EHS risks throughout the life cycle of solar energy systems. This review underscores the importance of ongoing research, innovation, and regulatory oversight to ensure the sustainable and safe deployment of solar energy technologies in the transition towards a low-carbon future.

Work Accidents Analysis in Lathe Workshop - JSA with FMEA Approach

This study investigates the risk levels and potential for work accidents in manufacturing processes involving heavy equipment like lathes, which often lead to accidents due to non-compliance with safety regulations. Employing job safety analysis (JSA), the research aims to minimize accidents by assessing risk levels and proposing comprehensive Personal Protective Equipment (PPE) usage for workers. Results reveal that the welding process's failure mode severity is rated at 2, while lathe-related accidents, particularly exposure to debris, have the highest occurrence and effect analysis at 6. Limited PPE availability contributes to a low failure mode detection and effect analysis score of 7. This study underscores the critical need for implementing stringent safety measures to mitigate workplace accidents in manufacturing settings. Highlights : Comprehensive assessment: Utilizing job safety analysis (JSA), the study provides a detailed evaluation of potential work accidents in manufacturing processes, focusing on heavy equipment usage. Risk mitigation: The research emphasizes the importance of implementing stringent safety measures, including the complete utilization of Personal Protective Equipment (PPE), to minimize the occurrence of workplace accidents. Importance of compliance: Non-compliance with safety regulations, particularly regarding the use of PPE and adherence to K3 regulations, significantly increases the risk of work accidents, leading to disruptions in production processes. Keywords: Workplace Safety, Manufacturing, Job Safety Analysis, Personal Protective Equipment, Risk Assessment

Electronic monitoring of working time and labour market outcomes: Evidence from Brazil

AbstractThis study analyses the dynamics of registered firms after implementing a new law (2009) in the Brazilian labour market. The law proposed the electronic monitoring of working time to provide more efficiency and security by standardizing the equipment used for control and avoiding manipulation. As stricter monitoring may increase costs, this paper seeks to analyse whether there have been changes in the dynamics of firms adapting to the new regulation. There are two potential mechanisms as a reference: increasing wages and decreasing workers (or contracted hours), and increasing labour force and decreasing contracted hours per worker (and therefore the monthly wage). Using Brazilian employee–employer data and the difference‐in‐differences approach as an empirical strategy, our main findings suggest that firms that adapt to the new electronic workday control had a general reduction in the number of hours hired, an increase in wages for companies with 10–50 employees and a decrease in workplace accidents in firms with 50 or more employees.

Profile of registered cases of occupational exposure to HIV in a referral hospital

A Workplace Accident involving Biological Material (WBA) is one in which the worker comes into contact with potentially contaminated blood and organic fluids. The aim was to determine the profile of cases of occupational exposure to the HIV virus in a referral hospital. This is a retrospective documentary study with a quantitative approach, carried out at the Clementino Fraga Hospital Complex. The population universe of 270 occupational accident records in 2018 indicated this number of cases that were treated at the Hospital Complex, resulting in a sample of 159 medical records. The proposed instrument was a form containing objective questions that addressed socioeconomic data and the objectives of the study. Data collection took place in March and April 2019. It was approved by the Research Ethics Committee. The documents collected showed that the profile of the sample investigated was predominantly female (86%), aged between 20 and 40 (73%), brown (91%) and nursing technician (47%). Considering the object of the study, it was found that in 53% of cases exposure was reported within 12 hours, in 87% the type of exposure was percutaneous, in 59% the organic material involved was blood, in 58% a needle with a lumen was the agent and in 25% improper disposal of sharp material was the most common circumstance. Turning to the preventive aspect, gloves were the PPE most used by professionals in their work (32%). However, a worrying fact was the almost proportional percentage of professionals who reported not using any type of PPE (30%). In addition, 79% of the workers had complete vaccination status, 42% of the cases did not know the source patient and, among the sample of the source patient variable, 52% had an unknown serological result. With regard to the outcome of the accident, 43% of the documents indicated the need for ART, 50% of the sample abandoned the treatment outlined by the doctor and 35% were discharged due to the negative serology of the source patient. Based on the main findings, we would like to draw attention to the need to start prophylaxis in the first two hours after the accident, the need to be careful when disposing of materials perforated during work activities, the need to be vigilant when using mandatory PPE in care practices, the need to know the source patient when carrying out the protocol and the need to actively seek out accident victims who abandon the recommended care.

The relationship between the rate on unemployment and occupational injuries: A case of Russia

This study aims to reveal how growth or decline in the rate of unemployment determines occupational injuries in Russia. Based on the regional statistics from 2009 to 2021 with the help of linear panel models we estimate the relationship between the rate of unemployment and fatal or non-fatal occupational injuries rates. The results show that when the economy is decreasing employees or employers prefer to underreport workplace accidents. This effect is especially strong in conditions of rising unemployment due to the release of workers, layoffs or liquidation of enterprises. At the same time, the fluctuations of unemployment do not change the fatal accidents; that is, injuries per se do not have a cyclical behavior. Thus, though there is a decreasing trend in the rate of occupational injuries in Russia, there is a problem of underreporting of accidents as we observe in high-income economies.

Relationship Between Companies' Responses to Near-Miss Reports and Turnover Intentions of Workers: A Nationwide Cross-Sectional Study

BackgroundEffective near-miss management is important in preventing workplace accidents. A company's inadequate response to near-miss reports can lead workers to feel insecure and dissatisfied with the company. We investigated the relationship between companies' responses to near-miss reports and turnover intentions of workers. MethodsWe conducted a cross-sectional study using online self-administered questionnaire survey to workers aged ≥20 years in Japan in March 2022. The analysis included 5,071 participants who had near-miss experiences and reported them to their companies. The independent variable was companies' responses to near-miss reports, classified into three categories: adequate response group, inadequate response group, and no response group. The dependent variable was turnover intentions. We calculated the odds ratio and 95% confidential interval (CI) using multilevel logistic regression analyses nested for industries and adjusted for covariates. ResultsOf the 5,071 participants, 3,058 (60.3%) were adequate response group, 1,484 (29.3%) were inadequate response group, and 529 (10.4%) were no response group. In multivariable adjusted model, compared with adequate response group, the odds ratio of inadequate response group and no response group were 1.80 (95% CI: 1.56–2.08) and 2.63 (95% CI: 2.15–3.22), respectively. ConclusionOur results suggested that there was a relationship between companies' responses to the near-miss reports and turnover intentions of workers. It is important not only to collect near-misses but also to respond appropriately to the reports and provide feedback to workers.

Relationship between Different Levels of Luminance and Color Temperature of LED Lamps on Human Error and Work Speed in Laboratory Conditions

Abstract Aim: The use of appropriate lighting systems can have a significant impact on improving cognitive performance parameters and reducing workplace accidents. The aim of this study was to examine the relationship between different levels of luminance and color temperature of LED lamps on human error and the work speed of individuals in laboratory conditions. Materials and Methods: This empirical study was conducted on 12 men and women volunteers. Two LED lamps with two color temperatures of 3000°K and 6000°K were set at angles of 15°, 30°, and 45° and with the same light intensity of 300 lux. Accuracy and work speed parameters were recorded at different time intervals using a target accuracy test. Statistical analysis was performed using SPSS version 26. Results: The mean ± standard deviation age of the participants in this study was 20.9 ± 1.2 years, and 50% (6 individuals) were male. The results showed that human error among people in three different angles of luminance for color temperatures of 6000°K and 3000°K was significant (P < 0.01), and in terms of the work speed, this difference was not significant (P > 0.05). The comparison of error counts and work speed between two color temperatures of 3000°K and 6000°K at all three angles of luminance independently showed a significant difference only in error counts (P < 0.001). Conclusion: The results demonstrated that the angle of luminance and color temperature have an impact on human error, with individuals exhibiting fewer errors at higher color temperatures. The findings of this research can be utilized in industries that involve high cognitive performance-sensitive occupations.

The Influence of Unsafe Behavior and Workspace Arrangement on Occupational Accidents Among Workers at Pt Maruki International Indonesia Makassar

Objective: Workplace accidents are unplanned and uncontrolled incidents that occur during work due to unsafe actions and unsafe conditions, leading to the interruption of work activities. This study aims to investigate the influence of behavior and workspace layout on workplace acidentes.
 
 Method: This research employs a quantitative approach with a case-control study design. Questionnaires were distributed to 57 workers, comprising 19 cases of workplace accidents and 38 controls without workplace accidents at PT Maruki Internasional Indonesia. Collected data include gender, age, tenure, behavior, and workspace layout. The gathered data in Excel tables will be analyzed using chi-square tests and logistic regression analysis.
 
 Result: The most common type of workplace accident is found among male workers (OR=4.065 [CI 95%: 0.462-35.752]; p=0.247). Unsafe work behavior (OR=18.417 [CI 95%: 3.656-92.779]; p=0.000) and workspace layout (OR=5.385 [CI 95%: 1.587-18.264]; p=0.010) are significant factors. Further logistic regression analysis reveals that variables influencing workplace accidents at PT Maruki Internasional Indonesia are work behavior and workspace layout
 Conclussion: Behavior and workspace layout are the most influential risk factors for workplace accidents, with unsafe work behavior being the most significant risk fator.
 
 Implication of the research: This research can contribute to raising public awareness about the importance of workplace accident impacts and encourage people to be more cautious while working.

Hazard Identification Using Task Risk Assessment Method and Bowtie Analysis (Case Study: PT. Varia Usaha Beton)

PT. Varia Usaha Beton is a subsidiary of PT. Semen Indonesia Beton, which produces various types of products. PT Varia Usaha Beton (BSP Purwokerto) has an open work area where there are potential hazards such as physical factors, chemical factors and ergonomic factors. The aim of this research is to identify hazards, assess risks and control risks in the production process of ready-mixed concrete so that workplace accidents can be minimized. The methods used in this research are task risk assessment and bowtie analysis. Based on the research results, it was found that 1 risk belonged to the low category, 27 risks belonged to the medium category, and 11 risks belonged to the high category. Based on the bow-tie method, the major risks are exposure to fly ash and cement dust, risk of diesel spills, and risk of electric shock. The main causes of workplace accidents are pipe leaks and improper diesel filling. The main risk effects of workplace accidents are worker injury, and fire. The bowtie analysis method determines that the risk of electric shock is due to a damaged cable and the existing protective barrier is intended to replace the damaged cable. This risk has consequences, namely injuries to workers.

An alternative approach to understanding occupational safety and health (OSH): an empirical insurance cost perspective in Italy

ABSTRACT In this article, we investigate the occupational safety and health phenomenon by implementing an alternative perspective that focuses on the economic dimensions of workplace accidents. To this end, we study the empirical relationship between the average amount of compensation paid per employee (or the average working days lost due to injuries) and a set of productive system, socioeconomic and institutional controls. We implement a fixed- and random-effects model estimation over a panel of 20 Italian regions for the 2010–2019 time span. Our main findings confirm that labour skills, educational controls and the regional level of law compliance play a crucial role in explaining the phenomenon under investigation.

Object Detection for Safety Attire Using YOLO (You Only Look Once)

Personal protective equipment (PPE) usage is mandated for all employees to prevent workplace accidents and foster a safe and healthy work environment. Using YOLOv8 machine learning and Google Colab's web-based development environment, this research aims to create an immediate detection system for PPE violations in the workplace. By keeping track of PPE compliance, the system is intended to increase workplace safety and prevent accidents. The dataset is collected through a mixture of real-life image gathering and internet datasets. Various images are collected that aim to train the model to detect objects from afar, close, and individually. The research methodology includes a review of the literature, the gathering, pre-processing, and training of models. According to the use of safety helmets, safety shoes, and gloves, there are three different classes of detection based on the bounding box. The system successfully detected the classes with an overall score above 0.8. The safety helmet achieved 0.969, the safety gloves achieved 0.857, followed by the safety vest with 0.887. The findings from this study indicate that the developed system can effectively improve occupational safety and health management. However, there is a detection error factor caused by the lighting and colors. Future research can focus on integrating the system with other work safety systems to provide a comprehensive solution for accident prevention.

Analisis K3 pada Mesin Milling, Mesin Bubut dan Ruangan Produksi di PT. Z Menggunakan Metode HIRARC

Occupational safety and health play a crucial role in maintaining productivity, sustainability, and the reputation of a company. Unsafe or unhealthy working conditions can lead to workplace accidents, injuries, or even fatalities. Additionally, the costs associated with work-related accidents or illnesses can be significant, including medical expenses, workers' compensation, and lost productivity. Therefore, it is important for companies to have an effective occupational safety and health program to protect their employees. One method used to analyze and manage occupational safety and health risks is HIRARC (Hazard Identification, Risk Assessment, and Risk Control). In this report, we will discuss the background of occupational safety and health and explain the steps of HIRARC in identifying, assessing, and controlling occupational safety and health risks in the workplace. The HIRARC method serves as an effort to prevent or minimize workplace accidents and includes a combination of Hazard Identification, followed by Risk Assessment to evaluate potential hazards in milling machines, lathe machines, and production areas, and finally Risk Control to implement measures that can minimize workplace accidents in the working environment. Keywords: HIRARC (Hazard Identification, Risk Assessment, and Risk Control), Occupational safety and health, Production area

Analisis Pemeliharaan Sumber Daya Manusia Kesehatan (SDMK) di PT Tirta Medical Indonesia

Human resource maintenance is very important for every company, because it not only helps to retain human resources, but also can increase dedication, discipline, and employee loyalty. Human resource maintenance will protect workers from workplace accidents and maintain communication to remain harmonious among human resources. This research uses a qualitative method and deductive approach, where the informants explain the principles of learning content and its application in specific situations. The purpose of this study is to determine the procedures for implementing human resource management maintenance at PT Tirta Medical Indonesia. From the results of the study, it can be seen that PT Tirta Medical Indonesia has implemented good work maintenance methods in accordance with the theory of Drs. H. Melayu S. P Hasibuan, namely effective communication between the company and employees, fair incentives, attention to employee welfare, health, occupational safety, and Pancasila industrial relations. The author recommends that PT Tirta Medical Indonesia continue to maintain and improve human resource maintenance for better employee loyalty and performance.

The impact of safety culture dimensions on workplace accidents: an application in the Moroccan automotive industry

Introduction: There is a general accord that safety culture is multidimensional. but limited research and publications are there about those dimensions. Almost one-third of the publications presenting safety culture definitions do not discuss the safety culture dimension nor the relationship between the safety culture dimensions and workplace accidents. To further understand the factors influencing this correlation. it appeared appropriate in this situation to investigate the relationship between the safety culture dimensions and occupational accidents. This study aims to identify the safety culture dimensions in the automotive industry in Morocco and its relations with occupational accidents. Methods: A study was done with 35 of the largest automotive companies in Morocco. the effects of Safety Culture dimensions on workplace accidents in the automotive industry were examined based on the literature review. and a measurement questionnaire that was created with a sample of numerous automotive workers for 2 months in the winter semester of November and December 2022. Results: The results highlight that the safety culture dimensions have a strong influence on avoiding accidents especially the safety culture's dimensions which present 40%. In addition, the COVID-19 period had a big impact on the number of occupational accidents in the automotive sector. 40.54% of these occupational accidents occurred between 2020 and 2022. Conclusion: Based on the results gained from the Questionnaire the common safety culture dimensions are employees’ attitudes or unsafe behaviors, lack of staff participation, and inadequate supervision.