Industrial Accidents Research Articles

Fabrication of mechanically stable fabric-based hydrogel membranes based on dual network toughening strategy for high efficient oil–water emulsion separation

Massive discharge of oily wastewater originating from oil-related industry, residential sewage and frequent oil spill accidents leads to serious environmental problems and health concerns. Amphoteric polyelectrolytes and hydrogel materials have been employed in the context of oil–water separation to resist oil pollution. However, the weak mechanical properties limit their practical application in complex environments. To address this, the design of a mechanically stable superhydrophilic/underwater superoleophobic composite membrane (PSBMA/PAM@CF) was been proposed by ingeniously in-situ growth of 3- [N, N-dimethyl −[2-(2-methylpropyl-2-enoxyloxy) ethyl] ammonium] propane-1-sulfonate (PSBMA)/polyacrylamide (PAM) dual network (DN) hydrogel on cotton fabric. The DN hydrogel strategy increased the stress of PSBMA hydrogel from 0.200 Mpa to 5.80 Mpa. The toughening effect of the DN hydrogel and its entanglement with cotton fibers endowed the membranes with excellent mechanical properties, and resulted in stable underwater superoleophobic and antifouling properties even after mechanical stability test. The PSBMA/PAM@CF membrane exhibited a separation efficiency exceeding 98.0 % for all types of oil-in-water emulsions stabilized by surfactants, and the membrane’s permeance remained at 1.75 × 103 L·m−2·h−1·bar−1 after 30 cycles. Additionally, the membrane exhibited a high permeance recovery rate of 98.2 %, surpassing general membranes. More significantly, we visualized the separation process through fluid dynamics simulations and revealed the separation mechanism of oil–water emulsions by membranes. It is expected that this work will provide new prospects for designing anti-oil-fouling membranes and applying in practical oil/water separation.

Analisis Penerapan K3 di Pabrik Perkebunan Sawit

. Implementing a culture of safety or safe work in oil palm plantations is very difficult for the workforce, especially those with low education. The palm oil industry faces many risks, including heat exposure, heavy workloads, exposure to hazardous chemicals, fires, and accidents due to heavy equipment. OHS is still seen as a cost and not an investment to prevent work accidents in companies or industries. The purpose of this study was to summarize the journal and find important information about the implementation of OSH in palm oil mills. In writing this journal, a literature review approach was used. The journals used in this study came from journals accessed through Google Schoolar. The results showed that there are several factors that cause To conduct this research, the activities needed to identify risks in the work process, such as fire, fall risk, heat exposure, chemical exposure, and occupational accidents. To conduct this research, the necessary activities to identify risks in the work process at the.

Detection and segmentation of wire rope surface deficiency based on YOLOv8 and U-Net

Abstract The presence of surface defects in wire ropes (WR) may lead to potential safety hazards and performance degradation, necessitating timely detection and repair. Hence, this paper proposes a method for detecting surface defects in WR based on the deep learning models YOLOv8s and U-Net, aiming to identify surface defects in real-time and extract defect data, thereby enhancing the efficiency of surface defect detection. Firstly, the ECA attention mechanism is incorporated into the YOLOv8 algorithm to enhance detection performance, achieving real-time localization and identification of surface defects in WR. Secondly, in order to obtain detailed defect data, the U-Net semantic segmentation algorithm is employed for morphological segmentation of defects, thereby obtaining the contour features of surface defects. Finally, in conjunction with OpenCV technology, the segmentation results of the defects are quantified to extract data, obtaining parameters such as the area and perimeter of the surface defects in the WR. Experimental results demonstrate that the improved YOLOv8-ECA model exhibits good accuracy and robustness, with the model’s mAP@0.5 reaching 84.78%, an increase of 1.13% compared to the base model, an accuracy rate of 90.70%, and an FPS of 65. The U-Net model can efficiently perform segmentation processing on surface defects of WR, with an mIOU of 83.54% and an mPA of 90.78%. This method can rapidly, accurately, and specifically detect surface defects in WR, which is of significant importance in preventing industrial production safety accidents.

Inspection of Semi-Elliptical Defects in a Steel Pipe Using the Metal Magnetic Memory Method

Ferromagnetic pipes are widely used for fluid transportation in various industries. The failure of these ferromagnetic pipes due to surface defects can generate industrial accidents, economic losses, and environmental pollution. Non-destructive testing techniques are required to detect these surface defects. An alternative is the metal magnetic memory (MMM) method, which can be employed to detect surface flaws in ferromagnetic structures. Based on this method, we present an analysis of experimental results of the magnetic field variations around five different surface semi-elliptical defects of an ASTM A36 steel pipe. A measurement system of MMM signals is implemented with a rotatory mechanism, a magnetoresistive sensor, a data processing unit, and a control digital unit. The MMM method does not require expensive equipment or special treatment of the ferromagnetic structures. In order to research a potential relationship between the defect sample size and the measured MMM signals, variable defect dimensions are experimentally considered. According to these results, the shape and magnitude of the normal and tangential MMM signals are altered by the superficial semi-elliptical defects. In particular, the maximum and mean tangential components and the maximum and minimum normal components are related to the defect dimensions. The proposed measurement system can be used to study the behavior of magnetic field variations around surface defects of ferromagnetic pipes. This system can be adapted to measure the position and damage level of small defects on the surface of ferromagnetic pipes.

Critical Factors Contributing to Under-Reporting of Occupational Accidents in The Construction Industry in Ghana

Purpose: Governments around the world have a shared responsibility for the management of occupational health and safety (OHS). The study aims to explore the under-reporting of OHS accidents by employees and employers in the construction industry in Ghana. Design/ Methodology/ Approach: The study used a cross-sectional survey design and quantitative and qualitative research techniques. Data was gathered via a questionnaire. The survey involved 250 site managers and operatives from Upper West and Upper East Regions. Significant factors that contribute to the under-reporting of construction-related accidents were identified and prioritized using descriptive statistics. The Linear Structural Relationships (LISREL) factor analysis was used to confirm the important factors. Findings: It was deduced from the findings that job security, workers’ attitudes, education and training, blame culture, interpersonal relationships, poor safety culture, lack of management commitment, poor communication, and company’s goal contribute significantly to the under-reporting of OHS accidents within construction firms in Ghana. In the study, factors contributing to the under-reporting of construction site accidents to statutory authorities included education and training, the environment, politics/government, legal/regulations, and socio-cultural practices. Practical Implications: The findings will enable construction companies to make investments to develop accident reporting systems that are easier to use and more accessible. This may include developing mobile reporting apps, and training employees how to use the reporting apps and tools efficiently. Social Implications: Employee safety and well-being is one of the main societal ramifications. Policymakers can use the findings to develop more robust policies aimed at improving accident reporting and prevention measures in the construction industry. Originality and Value: This research offers valuable insights into the factors influencing the underreporting of occupational health and safety (OHS) accidents on construction sites.

Development of a resilience safety culture model for the hazardous materials processing industry

Many previous cases have demonstrated that hazardous materials processing industry (HMPI) accidents always result in huge losses and HMPI companies which suffer accidents are difficult to quickly restore. This has demanded an effective way to solve the two problems. Resilience safety culture (RSC) combines conventional safety culture and resilience engineering. It aims at giving an organization the abilities of continually operating or timely recovering during or after a major unexpected event. RSC fits the safety demands of the HMPI well. The present study develops a RSC model for the HMPI. Firstly, through literature review, 12 model elements with 16 hypothesized relationships are developed. These hypothesis are then tested by 618 samples with structural equation modelling. Finally, an HMPI RSC model with 12 elements and 12 relationships is established. Model validity is verified by a comparison study on the proposed model and two existing models with the fuzzy analytic hierarchy process.

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

Unsafe behaviors among construction workers are a leading cause of safety accidents in the construction industry, and studying the mechanism of unsafe behavior propagation among construction workers is essential for reducing the occurrence of safety accidents. Safety attitude plays a pivotal role in predicting workers’ behavioral intentions. We propose a propagation model of unsafe behaviors based on a two-layer complex network, in which the upper layer depicts the change in construction workers’ safety attitudes, and the lower layer represents the propagation of unsafe behaviors. In this model, we consider the impact of individual heterogeneity and herd mentality on the transmission rate, establishing a partial mapping relationship based on behavioral feedback. After that, by building a probability transition tree, we establish the risk state transition equation in detail using the microscopic Markov chain approach (MMCA) and analyze the established equations to deduce the propagation threshold of unsafe behaviors analytically. The results show that enhancing the influence of individual heterogeneity and behavioral feedback increases the threshold for the spread of unsafe behaviors, thereby reducing its scale, while herd mentality amplifies the spread. Furthermore, the coexistence of safety education and behavioral feedback may lead to one of the mechanisms fails. This research enhances understanding of the propagation mechanism of unsafe behaviors and provides a foundation for managers to implement effective measures to suppress the propagation of unsafe behaviors among construction workers.

Identifying Vital Factors for Enhancing Safety Communication among Foreign Construction Field Workers

Enhancing safety communication within the construction industry is of paramount importance due to its potential in curtailing occupational injuries and improving the overall well-being of construction field workers. While the importance of improved communication is apparent, few studies have been focused on identifying the factors that positively influence communication, particularly in the context of safety. Especially in the case of foreign construction field workers (FCFWs), who often face communication challenges stemming from language and cultural differences, performing labor tasks in harsh and constantly changing environments is contributing significantly to the increasing rate of industrial accidents. Therefore, this study is aimed at investigating the vital factors that impact safety communication among FCFWs. A phenomenological qualitative method was applied to determine the vital factors influencing the safety communication among FCFWs. On applying the analytical hierarchy process, the factors and their importance were determined, and the vital factors were identified. Quantitative assessment through the analytic hierarchy process (AHP) established that extrinsic motivation (weight: 0.513), management communication style (0.264), and visible safety information (0.127) significantly overshadow other factors in safety communication effectiveness, validating their pivotal roles. However, a major limitation is that interview data were collected from workers of only four nationalities. Future studies should address this by expanding the range of nationalities included to enhance the diversity of experiences and perspectives from a broader variety of foreign construction field workers.

Global dioxin research trends and focal points: A century-long visual and bibliometric analysis (1923-2022).

Dioxin-like compounds, recognized by the World Health Organization (WHO) as among the most enduring toxic chemical substances in the environment, are linked to various occupational activities and industrial accidents worldwide. The aim of this study was to examine and present research publications on dioxins, pinpoint current research trends, identify research gaps, and highlight potential avenues for future exploration in the field. The study period for relevant research articles ranged from 1923 to December 31, 2022, and these articles were sourced from the Scopus database. The analysis involved the identification of key contributors to the field and the visualization of topics, themes, and international collaboration. VOSviewer software (version 1.6.20) was used for visualization analysis. A total of 11,620 publications on dioxins were documented in the Scopus database. The predominant category of these documents comprised 9780 original articles, which represents 84.17% of the total publications. The United States lead in the number of publications, with 3992 (34.35%), followed by Japan, with 1429 (12.3%), China, with 1005 (8.65%), and Germany, with 974 (8.38%). Before 2002, scholarly attention in this field focused primarily on the health effects, environmental fate, and mechanism of toxicity of tetrachlorodibenzo-p-dioxin (TCDD). However, a noticeable change in research focus has been observed since 2002, highlighting the emergence of a topic related to the health effects and environmental fate of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PFDFs). This study is the first to conduct a comprehensive quantitative bibliometric analysis of dioxins over time. These findings indicate a significant increase in the overall growth of the dioxin literature over the past 30 years. These findings may prove crucial in guiding and organizing subsequent investigations related to dioxins.

ANALYSIS OF THE LEVEL OF OCCUPATIONAL INJURIES

Problem statement. The task of the research is evaluation the dynamics of changes in the level of injuries at work depending on the main causes of the accident. Technical, organizational, psychophysiological, technogenic, social, natural and environmental causes are closely interrelated and have a significant impact on the number of victims of accidents at the industrial plants, as well as on the number of victims of accidents with fatal consequences. The purpose of the article. The assessment of changes in industrial injuries at Ukrainian enterprises during 2010−2022. The establishing connection between social and industrial factors affecting the rate of industrial accidents. The creation of correlation-regression models for statistical evaluation and analysis of the influence of factor variables on the results of injury. Methodology. The use of descriptive statistics for the analysis of the dynamics of changes in the level of industrial injuries. Carrying out a correlation analysis to establish the density of connection between factor variables and resulting features. The use of regression-variance analysis to obtain coefficients of regression mathematical models and statistical indicators that explain the probability of significance of these coefficients. Calculation of the value of the relative error of the calculation data obtained according to mathematical models to confirm their adequacy. Scientific novelty. Multiple correlation-regression models have been developed that take into account the main causes of the accident as factor variables affecting the injury rate and the rate of fatal accidents. Practical significance. Mathematical models make estimate the impact of the main causes of accidents on the level of industrial injuries. The implementation of improved methods and means of safe activity at work is an important tool for correcting the cause-and-effect relationships of industrial injuries. Conclusions. The correlation-regression models were created to analyze the level of industrial injuries in Ukraine. The numerical calculations were carried out according to these models. The average values of the relative errors of the calculated data are 1.55 % and 6.08 %, which indicates the adequacy of the developed models.

Validity and reliability of a questionnaire to investigate people prone to accidents in different industries

Objectives. In recent years, workplace accidents have imposed a considerable cost on industries. Accident proneness is one of the most influential factors known to cause unsafe behaviors and occupational accidents. Therefore, the present study investigated the validity and reliability of the accident proneness questionnaire in industries. Methods. The questionnaire was developed to measure accident proneness in industries – the developed instrument was distributed in East Azarbaijan industries (1100 questionaries). Content validity, confirmatory factor analysis and exploratory factor analysis were used to examine the construct validity, and Cronbach’s α and the test–retest method were used to examine the reliability and reproducibility of the instrument. Results. In the face validity section, the impact score of all items was determined to be >1.5. In the content validity section, the content validity index (CVI) and the content validity ratio (CVR) were higher than the standard value of 0.42. The Cronbach’s α coefficient for all dimensions was 0.914. Conclusion. The findings of the study show that the accident proneness questionnaire in industries has acceptable validity and reliability, and can be used as a comprehensive, practical, robust and reliable instrument to investigate the accident proneness of different industries.

A Scoping Review of Causal Associations between Occupation and Cancer Occurrence and Legal Burden of Proof.

Occupational diseases, characterized by the gradual accumulation of work-related harmful effects over extended periods, often lack a distinct, identifiable incident causative of the disease. This ambiguity in pinpointing the work-relatedness of such diseases stems from the intricate interplay between occupational risks, workers' pathophysiological predispositions, and pre-existing health conditions, all of which evolve slowly over time. Consequently, establishing a definitive causal relationship between occupational exposure and disease manifestation becomes a pivotal, yet challenging, aspect in securing industrial accident insurance benefits. In contrast to occupational accidents, where causality is relatively more discernible, the complexity escalates in the context of occupational diseases. Typically, employers maintain the majority of data pertinent to establishing causality, but this data is frequently inadequate. Furthermore, the onus of proving the work-relatedness of a disease falls on the worker, a process that necessitates specialized medical knowledge, thereby compounding the difficulty. Imposing the burden of proof on workers in occupational disease litigation could lead to a lapse in worker protection. This paper critically explores methodologies to safeguard workers, focusing specifically on the burden of proof concerning causality in occupational diseases. This analysis aims to highlight the challenges workers face in establishing a connection between their work and disease, proposing potential legal and policy solutions to ensure more equitable and just outcomes in occupational disease claims.

A STUDY ON THE RELATIONSHIP BETWEEN LABOUR WELFARE SCHEMES AND ITS IMPACT ON JOB SATISFACTION AMONG WORKERS IN TITAN WATCH COMPANY, HOSUR – A CASE STUDY

In an industrial economy, the workers are subject to periodic unemployment due to cyclical fluctuations in business, sickness, industrial accidents and old age. In India it has the obligation from the Government side to help the labourers and provide them security and adopt labour welfare measures in the form of various schemes such as Workmen’s Compensation Act, Employees’ State Insurance Act, Finances and Contributions, Sickness benefit, Medical benefit, Maternity benefit, Disablement benefit, Dependents’ benefit, Employees’ Provident Fund and Bonus schemes, Gratuity Scheme and Employees’ Pension Scheme. All these welfare schemes have followed and adopted in industries for the welfare of labourers both men and women. So, the present study is done to understand the various labour welfare facilities such as statutory and non-statutory welfare facilities provided by the company to labourers / workers namely TITAN watch company, Hosur, and its impact on job satisfaction. This study shows the positive relation between welfare facilities such as medical facilities, crèche, canteen facilities, recreation, toilet facilities, rest room facilities, reading room, loan facilities for their children’s education, marriage, housing loan, insurance coverage etc. and job satisfaction. Job satisfaction is very important to know the level of contribution to workers towards his / her work and how welfare facilities affect. Labour welfare schemes are provided to strengthen man power in both physically and mentally. Thus, these facilities are more effective in improving the performance and building the morale of every worker working in industries, in order to increase the productivity of labourers.

A deep neural network algorithm-based approach for predicting recovery period of accidents according to construction scale

Despite ongoing safety efforts, construction sites experience a concerningly high accident rate. Notwithstanding that policies and research to reduce the risk of accidents in the construction industry have been active for a long time, the accident rate in the construction industry is considerably higher than in other industries. This trend may likely be further exacerbated by the rapid growth of large-scale construction projects driven by urban population expansion. Consequently, accurately predicting recovery periods of accidents at construction sites in advance and proactively investing in measures to mitigate them is critical for efficiently managing construction projects. Therefore, the purpose of this study is to propose a framework for developing accident prediction models based on the Deep Neural Network (DNN) algorithm according to the scale of the construction site. This study suggests DNN models and applies the DNN for each construction site scale to predict accident recovery periods. The model performance and accuracy were evaluated using mean absolute error (MAE) and root-mean-square error (RMSE) and compared with the widely used multiple regression analysis model. As a result of model comparison, the DNN models showed a lower prediction error rate than the regression analysis models for both small-to-medium and large construction sites. The findings and framework of this study can be applied as the opening stage of accident risk assessment using deep learning techniques, and the introduction of deep learning technology to safety management according to the scale of the construction site is provided as a guideline.

일본의 산업재해 예방과 행정조직 및 책무

일본의 산업재해 예방과 행정조직 및 책무

Evaluation of Disaster Perception Levels of University Students: A Sample Application Study

Disasters are important events that are difficult to predict and occur in various ways, often with high destructive power. Earthquakes, floods, droughts, major industrial accidents, famine, technological accidents, epidemics, etc. are only a few of the major types of disas-ters that mankind has been exposed to from past to present. In recent years, natural and unnatural disaster events have been observed both in Turkey (Düzce-earthquake, 1999; Giresun-Dereli-flood-flood, 2020; Kahramanmaraş-Pazarcık-earthquake, 2023) and in many other parts of the world (Sumatra-tsunami, 2004; Ecuador-earthquake, 2016; USA-hurricane-storm, 2023). The impact of disasters is often not limited to one region but can spread over a wide area. As a matter of fact, the impact of this destruction can be reduced at some point thanks to various measures that can be taken before the disaster. For exam-ple, identification of regional risks, early warning systems, crisis management, emergency action plans, etc. can be considered within the scope of these measures. Another important issue is to increase the level of disaster awareness of the society. Especially in regions where disaster risk maps have been prepared, such awareness raising activities can be carried out to make the reaction of the society against disasters more controlled and conscious. The aforementioned issues constitute the main objective of this study. This study was conducted to evaluate the disaster awareness levels of Gümüşhane Univer-sity central campus students. The data obtained in the study were analyzed using SPPS 25 statistical program. In the study, a 5-point Likert scale was used to measure disaster risk perception. In the study, frequency analysis test, Kruskal Wallis test and Mann Whitney U test were performed. As a result of the analysis, it was analyzed at 5% level of significance (p<0.05) and it is stated in the tables as arithmetic mean, standard deviation and percent-age of the number of individuals. Accordingly, as a result of the non-parametric Mann Whitney-U test, no significant difference was found between the groups. As a result of the non-parametric Mann Whitney-U test conducted to determine whether the disaster risk perception sub-dimension scores of the students differed significantly according to the gender variable, a statistically significant difference was found between the groups at the p<0.05 level in favor of the group exposed to disaster.

Expanding hematoma with facial nerve palsy: an unusual complication of mandibular fracture

The mandible is one of the most commonly fractured facial bones, along with the nasal and zygomatic bones. The most common cause of fracture is trauma, such as a motor vehicle accident, a physical altercation, an industrial accident, a fall, or a contact sport injury. Because of its position and the complex anatomy and environment surrounding it, the mandible is extremely vulnerable and often breaks when a traumatic injury is made to the face. Complications of mandible fractures can range from 7 to 29%, depending on the severity of the injury, the location of the injury, and the number of areas affected. The most frequently encountered complications include infection, hardware failure, osteomyelitis, non-union, mal-union, and wound dehiscence. Here, we report an unusual complication of an expanding hematoma with left sided facial nerve paresis that resulted after 2 months of mandible fracture.

Актуальное в судебной практике по делам о возмещении вреда, причиненного жизни и здоровью работников

The article attempts to summarise judicial practice in cases of compensation for damage caused to the life and health of an employee in order to identify the main attributes of industrial accidents that distinguish them from other cases of health harm.

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

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

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

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