• Journal of Environmental Science International
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• v.17 no.3
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• pp.297-303
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• 2008

For last couple of years many apartments, roads and bridges are constructed along the Kok-kyo stream which is penetrating Asan city, Chung-Nam Province. The effect of construction on stream environment was investigated in this study. Various kinds of water qualities of the Kok-kyo stream were monitored for 5 months. As it passed through the construction sites, $COD_{cr}$ concentration increased abruptly whereas the increment of the $BOD_5$ was not so great, indicating that non-biodegradable organics were flowed from the construction sites to the stream. Particularly, 0.2 mg/L of N-hexane was measured, which is believed to be mainly responsible for the organic pollution from construction sites. In spite of short periods of water quality monitoring, it could be concluded that there was a good correlation between construction sites and stream pollution. Therefore, legislative activity or enforced management by local government to the construction sites are needed for keeping the small stream environment better.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.106-116
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• 2024

Purpose: This paper conducts IoT and CCTV-based safety monitoring to analyze accidents and potential risks occurring at construction sites, and detect and analyze risks such as falls and collisions or abnormalities and to establish a system for early warning using devices like a walkie-talkie and chatbot service. Method: A safety management service model is presented through smart construction technology case studies at the construction site and review a relevant literature analysis. Result: According to 'Construction Accident Statistics,' in 2021, there were 26,888 casualties in the construction industry, accounting for 26.3% of all reported accidents. Fatalities in construction-related accidents amounted to 417 individuals, representing 50.5% of all industrial accident-related deaths. This study suggests implementing AI chatbot services for construction site safety management utilizing IoT-based health monitoring technologies in smart construction practices. Construction sites where stakeholders such as workers participate were demonstrated by implementing an artificial intelligence chatbot system by selecting major risk areas within the workplace, such as scaffolding processes, openings, and access to hazardous machinery. Conclusion: The possibility of commercialization was confirmed by receiving more than 90 points in the satisfaction survey of participating workers regarding the empirical results of the artificial intelligence chatbot service at construction sites.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.55-56
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• 2020

The construction industry has the highest occupational fatality and injury rates related to accidents of any industry. Accordingly, safety managers closely monitor to prevent accidents in real-time by installing surveillance cameras at construction sites. However, due to human cognitive ability limitations, it is impossible to monitor many videos simultaneously, and the fatigue of the person monitoring surveillance cameras is also very high. Thus, to help safety managers monitor work and reduce the occupational accident rate, a study on object recognition in construction sites was conducted through surveillance cameras. In this study, we applied to the instance segmentation to identify the classification and location of objects and extract the size and shape of objects in construction sites. This research considers ways in which deep learning-based computer vision technology can be applied to safety management on a construction site.

• Disaster Prevention Review
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• v.14 no.5
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• pp.158-159
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• 2012

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.261-262
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• 2023

Drone is a suitable equipment for capturing images of cracks at construction sites based on its efficient mobility and high-resolution image acquisition capabilities. In this study, drone was used to acquire indoor construction sites framework images and deep learning technology was applied to detect cracks and measure width, and size. Finally, the usability of the process was verified based on the indoor crack monitoring system.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.154-162
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• 2022

Purpose: The government intends to come up with measures to revitalize safety management at construction sites to shift safety management at construction sites from human capabilities to system-oriented management systems using advanced technologies AIoT and Mobile technologies. Method: The construction site safety management monitoring system using AIoT and Mobile technology conducted an experiment on the effectiveness of the construction site by applying three algorithms: virtual fence, fire monitoring, and recognition of not wearing a safety helmet. Result: The number of workers in the experiment was 215 and 7.61 virtual fence intrusion was 3.5% compared to the number of subjects and 0.16 fire detection were 0.07% compared to the subjects, and the average monthly rate of not wearing a safety helmet was 8.79, 4.05% compared to the subjects. Conclusion: It was found that the construction site safety management monitoring system using AIoT and Mobile technology has a valid effect on the construction site.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.361-369
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• 2022

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.27 no.1
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• pp.46-58
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• 2017

Objectives: Particulate matter (PM) at construction sites mostly originates from either construction activities or the atmospheric environment. This study was conducted to evaluate the exposure level to PM and the contribution ratio of atmosphere sources at construction sites. Methods: We reviewed literature and governmental systems related to PM exposure in occupational settings and summarized them. In the field evaluation, five construction sites and one golf course were selected: two from Gyeonggi-do Province and four from North Gyeongsang-do Province. For each site, personal samples from outdoor construction workers and area samples from the outdoor area around the construction site office were collected according to construction work types. PM concentrations reported from nearby National Ambient Air Monitoring Stations were recorded. Respirable dust concentrations, respirable silica concentrations, and several metal concentrations including Cd, Cr, Pb, and As were monitored over four months. In the end we suggested how to manage particulate matter exposure at construction sites. Results: There was little literature reporting on exposure levels of construction workers to PM. Respirable dust concentrations measured in Gyeonggi-do Province were higher than those measured in North Gyeongsang-do Province. The geometric means of respirable dust concentrations in personal samples and area samples were $37.89{\mu}g/m^3$ and $92.86{\mu}g/m^3$, respectively. The respirable dust concentrations were higher than the PM concentrations reported from nearby National Ambient Air Monitoring Station. The geometric means of respirable silica concentrations of personal samples and area samples were $1.3{\mu}g/m^3$ and $1.1{\mu}g/m^3$, respectively. All metal concentrations were lower than 10% of individual Korean occupational exposure limits. Conclusions: Assuming that personal samples consisted of ambient PM and dust originating from work activities and area samples only collected ambient PM, we concluded that the dust exposure of outdoor construction workers originated 40.8% from the atmosphere and 59.2% from construction activities. PM exposure at construction sites should be controlled by employers, as in the case of outdoor heat stress. The Korean government needs to consider setting an occupational exposure limit for respirable dust.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.3 no.4
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• pp.43-51
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• 2000

This study was carried out to develop technologies of restoration and revegetation through monitoring the change of vegetation after setting up 3 type experimental sites on abandoned coal-mine lands. According to the results of 7 month's monitoring, sown species and a variety of invading species appeared at experimental sites. Moreover, the number of species, individuals and the ratio of coverage increased as time passed on. After 5 months, each experiment sites showed the result of successful revegetation. The most effective sites was treated by overspreading 1 cm loam soil on coal minesoil. As a result of this study, it is considered that the revegetation of abandoned coal-mine lands can be achieved effectively by making the minimum of foundation layer and improving the efficiency through mechanized construction. Besides, it is recommended to use native and early stage-succession species adapting themselves to coal minesoil, such as Gramineae, Leguminosae, Compositae and etc.

• Journal of the Korea Institute of Building Construction
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• v.9 no.4
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• pp.103-109
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• 2009

The construction industry has the highest death rate related to safety accidents of any industry. Furthermore, in contrast to other industries, where the death rate related to safety accidents has been steadily declining, both the death toll and the number of accidents in the construction industry have been on the rise. Construction accidents occur when various risks increase in an intertwined way to reach a tipping point; a moment when such factors cannot be tolerated any longer Conventional safety management methods have restrictions in terms of their ability to fully prevent all types of safety accidents. This research considers ways in which USN technology can be applied to safety management on a construction site, and derives a method of applying USN technology for safety management monitoring. The tasks related to safety management on construction sites, as well as the occurrence of accidents, are first analyzed. By analyzing the characteristics of construction accidents, the factors that must be a priority and the factors that can be a lower priority are derived. Finally, the configuration of a monitoring system for safety management on a construction site to which USN technology is applied is presented. It is expected that safety accidents can be prevented from occurring on construction sites by applying this cutting-edge USN technology.