• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2606-2613
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• 2022

Spent resin often exceeds radiation limits for safe disposal, creating a need for commercial-scale treatment techniques to reduce resin radioactivity. In this study, the radiological safety of a commercialized spent resin treatment device with a treatment capacity of 1 ton/day was evaluated. The results confirm that the device is radiologically safe in the event of an accident. This device desorbs ¹⁴C from the spent resin, allowing disposal as low-level waste instead of intermediate-level waste. The device also reduces overall waste by recycling the extracted ¹⁴C. Potential accident scenarios were explored to enable dose assessments for both internal and external exposure while preventing further spillage of the device and processing the spilled resin. The scenarios involved the development of a surface fracture on the resin mixture separator and microwave systems, which were operated under pressure and temperature of 0-6 bar and 0-150 ℃, respectively. In the case of accidents with separator and microwave device, the maximum allowable working time of worker were derived, respectively, considering external and internal exposures. When wearing the respirator corresponding to APF 50, in the case of the microwave device accident scenario, the radiological safety was confirmed when the maximum worker worked within 132.1 h.

• Journal of Drive and Control
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• v.19 no.4
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• pp.62-69
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• 2022

Construction machinery is exposed to accidents such as collisions, narrowness, and overturns during operation. In particular, mobile crane is operated only with the driver's vision and limited information of the assistant worker. Thus, there is a high risk of an accident. Recently, some collision avoidance device using sensors such as cameras and LiDAR have been applied. However, they are still insufficient to prevent collisions in the omnidirectional 3D space. In this study, a rotating LiDAR device was developed and applied to a 250-ton crane to obtain a full-space point cloud. An algorithm that could provide distance information and safety status to the driver was developed. Also, deep-learning segmentation algorithm was used to classify human-worker. The developed device could recognize obstacles within 100m of a 360-degree range. In the experiment, a safety distance was calculated with an error of 10.3cm at 30m to give the operator an accurate distance and collision alarm.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.88-95
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• 2022

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clustering-based anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.73-82
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• 2023

Optimization of Construction Site Layout Planning (CSLP) heavily relies on workers' travel paths. However, traditional path generation approaches predominantly focus on the shortest path, often neglecting critical variables such as individual wayfinding tendencies, the spatial arrangement of site objects, and potential hazards. These oversights can lead to compromised path simulations, resulting in less reliable site layout plans. While Deep Reinforcement Learning (DRL) has been proposed as a potential alternative to address these issues, it has shown limitations. Despite presenting more realistic travel paths by considering these variables, DRL often struggles with efficiency in complex environments, leading to extended learning times and potential failures. To overcome these challenges, this study introduces a refined model that enhances spatial navigation capabilities and learning performance by integrating workers' visibility into the reward functions. The proposed model demonstrated a 12.47% increase in the pathfinding success rate and notable improvements in the other two performance measures compared to the existing DRL framework. The adoption of this model could greatly enhance the reliability of the results, ultimately improving site operational efficiency and safety management such as by reducing site congestion and accidents. Future research could expand this study by simulating travel paths in dynamic, multi-agent environments that represent different stages of construction.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1187-1193
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• 2022

A construction site is a highly complex and constantly changing environment, where hazardous areas are difficult to detect if workers lack sufficient knowledge and awareness. Thus, frequent worker safety training is required. Numerous studies on using virtual reality (VR) for safety training were published. While they demonstrate the potential for improving the skills necessary to avoid accidents in the construction industry, they remain difficult to apply at actual construction sites. VR requires specialized hardware and software, limiting workers' access and restricting workers' participation in training sessions. As a result, this paper proposes multiple platforms for immersive virtual reality safety training (VRMP) based on Industry Foundation Classes (IFC) and web technologies such as immersive web (WebXR). The VRMP is compatible with mobile and desktop devices currently by workers and demonstrates scenario models familiar to workers. Also, it reduces development time by utilizing Building Information Models (BIM). Additionally, The VRMP collects data from workers in a virtual environment to assess each worker's safety level, assisting workers in effectively and comfortably gaining a better understanding and raising their awareness. This paper develops a case study based on the VRPM in order to assess its effectiveness.

• Safety and Health at Work
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• v.15 no.2
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• pp.200-207
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• 2024

Background: Workers are often exposed to hazardous heat due to their work environment, leading to various injuries. As a result of climate change, heat-related injuries (HRIs) are becoming more problematic. This study aims to identify critical contributing factors to the severity of occupational HRIs. Methods: This study analyzed historical injury reports from the Occupational Safety and Health Administration (OSHA). Contributing factors to the severity of HRIs were identified using text mining and model-free machine learning methods. The Multinomial Logit Model (MNL) was applied to explore the relationship between impact factors and the severity of HRIs. Results: The results indicated a higher risk of fatal HRIs among middle-aged, older, and male workers, particularly in the construction, service, manufacturing, and agriculture industries. In addition, a higher heat index, collapses, heart attacks, and fall accidents increased the severity of HRIs, while symptoms such as dehydration, dizziness, cramps, faintness, and vomiting reduced the likelihood of fatal HRIs. Conclusions: The severity of HRIs was significantly influenced by factors like workers' age, gender, industry type, heat index , symptoms, and secondary injuries. The findings underscore the need for tailored preventive strategies and training across different worker groups to mitigate HRIs risks.

• Journal of the Korean Society of Safety
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• v.31 no.1
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• pp.87-94
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• 2016

Recently, as Korean industrial structure is moving to the service job, the number of workers engaged in the service job is increasing slowly. According to the statistics by Ministry of Employment and Labor announced in June, 2013, the number of service job workers in Korea was 7,477,135 which accounted for 48.4% of total workers. The trend of this service job is expected to increase continuously in the future. According to the 2013 statistics by Ministry of Employment and Labor, the number of industrial accidents victims of industrial accidents in the service job was 30,526 which was the biggest number among the entire businesses. The victims in the service job accounted for 33.2% among the total number of industrial accidents and represented more than those in the manufacture and construction industry. The service job had various works and employment patterns and most service jobs are petty and are small-sized establishments and it is difficult to try voluntarily to prevent the industrial accidents. However, Korean occupational safety and health act was enacted in accordance with the construction and manufacture in which industrial accidents occurred frequently in the past. The support of the government for the industrial accident prevention is focused on the construction and manufacture. Therefore, the current service job is placed on the blind spot of the safety management. Raising the safety awareness of workers through the safety education is the most important in order to prevent the industrial accidents of the service job with many conventional/repeated disasters such as the conduction by a simple mistake. Accordingly, this study analyzed the features and accidents of the domestic service jobs through the literature survey and analyzed the institutional devices for the safety management of the domestic service job, and the safety management cases of foreign service jobs and compared with domestic systems. Considering demands for the basic safety education for service job workers, a questionnaire was conducted targeting the service job workers and the execution plan of the basic safety & health education targeting the service job workers was carried out through the brainstorming of trainers of worker in the service job.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.488-496
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• 2020

This study was based on nitrate chemical accidents at home and abroad. Toxic gases due to adverse reactions are generated in the workplace, laboratory, hospital, container damage, and chemical misinjection. Through a case review of possible situations and safety, this study analyzed various cases of accidents, accident status, accident type, cause of the accident, location of the accidents, etc. from 2014 to 2018. The plans for improvement in education and nitrate accidents were reviewed. As a result, 36 nitrate chemical accidents were investigated, including 16 careless worker accidents, eight transportation accidents, and 12 facilities shortages. Nitrate chemical accidents are occurring continuously. Based on this, the range of toxic effects using CARIS was calculated at the worst-case scenario, and the effective response range was measured through the damage impact range. For this purpose, the impact range was predicted based on the strengthening of safety education, emergency action plan and correlation, and the quantified data was identified. In addition, the reliability of the scope of impact was reviewed based on the correlation formula that could facilitate the evacuation of residents, and it was applied to actual accident scenarios of the workplace to present the effects of the accident response and preventive measures.

• Safety and Health at Work
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• v.8 no.3
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1242-1242
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• 2022

Annually, several construction workers fall ill, are injured, or die due to heat-related exposure. The prevalence of work-related heat illness may rise and become an issue for workers operating in temperate climates, given the increase in frequency and intensity of heatwaves in the US. An increase in temperature negatively impacts physical exertion levels and mental state, thereby increasing the potential of accidents on the job site. To reduce the impact of heat stress on workers, it is critical to develop and implement measures for monitoring physical exertion levels and mental state in hot conditions. For this, limited studies have evaluated the utility of wearable biosensors in measuring physical exertion and mental workload in hot conditions. In addition, most studies focus solely on male participants, with little to no reference to female workers who may be exposed to greater heat stress risk. Therefore, this study aims to develop a process for objective and continuous assessment of worker physical exertion and mental workload using wearable biosensors. Physiological data were collected from eight (four male and four female) participants performing a simulated drilling task at 92oF and about 50% humidity level. After removing signal artifacts from the data using multiple filtering processes, the data was compared to a perceived muscle exertion scale and mental workload scale. Results indicate that biosensors' features can effectively detect the change in worker physical and mental state in hot conditions. Therefore, wearable biosensors provide a feasible and effective opportunity to continuously assess worker physical exertion and mental workload.