• Magazine of RCR
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• v.14 no.1
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• pp.61-64
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• 2019

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.19 no.3
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• pp.182-194
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• 2009

This study was conducted to evaluate health status and to propose health protection measures of irregular plant construction workers in Yoesu National Industrial Complex (YNIC). The status of safety and health management was examined in five areas including safety and health education, work environment monitoring, health examination, health management record, and personal protective equipment (PPE) for plant construction workers. The safety training rate for plant construction workers was reached high at 91%, The training was mostly consisted of safety accident related things, but training on hazardous materials was found to be insufficient. Workplace monitoring results showed that the compliance rate for work environment for irregular construction workers was 54% and workplace monitoring during turnaround (TA) period with high risk of exposure to hazardous agents has not been implemented. While 61.4% of irregular workers received the general health examination but only 36.8% received the special health examination. The special health examination was found to be conducted only upon welders from 2-3 years ago. The issue of health management record upon irregular construction workers was not being implemented. In case of PPE, basic safety protective equipments such as safety shoes, safety belt, safety helmet were being supplied well while the supply rate of respirator for organic vapor was relatively low at 40%. Based on this study, two suggestions to maximize the utilization of the current safety and health program were made while boosting its effectiveness in protecting workers' health. First, the role of owners (petrochemical plant) related to safety and health should be strengthened. Second, in consideration of the characteristics of construction workers who usually engage in short term employment and frequent movement, community based health management organization is suggested that can overcome such structural problem and carry out the implementation of health examination and sustained health management.

• Journal of KIBIM
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• v.11 no.2
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.731-737
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• 2024

The accident rate in the South Korean construction industry has increased by 50% over the past ten years, reaching seven times the average growth rate of the entire industry. However, the number of management personnel at construction sites is decreasing, making it increasingly difficult to establish a safety monitoring system through professional personnel. This study aims to develop an intelligent control system to address the problem of insufficient management personnel and support the establishment of a continuous safety monitoring system. This system consists of a mobile information collection robot (S-BOT) and an intelligent algorithm. The visual information collected by S-BOT can be analyzed in real-time using computer vision-based intelligent algorithms to detect unsafe situations. The results of this study will contribute to preventing unnecessary social and economic losses by maximizing safety management efficiency and supporting timely decision-making through the sharing of information provided by the intelligent control system.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.61-69
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• 2016

Software testing is an activity to find defects included in software through creating test cases from the software system specification. In order to perform software testing effectively, it is required to prepare the full test plan, to create well-defined test cases, and to execute test monitoring activities systematically. Most existing researches for the test approaches focus on automating the activities from the test cases generation to the test execution. Contrary to those approaches, we study automatic approaches for test monitoring activities. For this, we identify the research issues that should be solved to automate test monitoring activities. Next, with those solutions, we suggest the construction method for an automatic framework for test monitoring.

• Journal of Korean Society of societal Security
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• v.2 no.2
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• pp.49-54
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• 2009

High incident of accidents in construction jobsite became a social problem. According to the International Labour Organization (ILO), more than 60,000 fatal accidents occur each year in construction workplace worldwide. This number of accidents accounts for about 17 percent of all fatal workplace accidents. Especially, accidents from struck-by and falls comprise of over 60 percent of construction fatalities. This paper introduces a prototype of a received signal strength index (RSSI)-based safety monitoring to mitigate the potential accidents caused by falls and struck-by. Correlation between signal strength and noise index is examined to create the distance profile between a transmitter and a receiver. Throughout the distributed sensor nodes attached on potential hazardous objects, the proposed prototype envisions that construction workers with a tracker-tag can identify and monitor their current working environment in construction workplace, and early warning system can reduce the incidents of fatal accident in construction job site.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.206-207
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• 2020

Accurate estimation of concrete strength development at early ages is a critical factor to secure structural stability as well as to speed up the construction process. The temperature generated from the heat of hydration is considered as a key parameter in predicting the early age strength. Conventionally, concrete temperature has been measured by temperature sensors installed inside concrete. However, considering the measurement on building structures with multiple floors, this method requires reinstallation and repositioning of hardware such as sensors, data loggers and routers for data transfer. This makes the temperature monitoring work cumbersome and inefficient. Concrete temperature monitoring by using thermal remote sensing can be an effective alternative to supplement those shortcomings. In this study, image processing was carried out through K-means clustering technique, which is a unsupervised learning method, and the classification results were analyzed accordingly. In the future, research will be conducted on how to automatically recognize concrete among various objects by using deep learning techniques.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.11-19
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• 2020

A daily progress monitoring and further schedule management of a construction project have a significant impact on the construction manager's decision making in schedule change and controlling field operation. However, a current site monitoring method highly relies on the manually recorded daily-log book by the person in charge of the work. For this reason, it is difficult to take a detached view and sometimes human error such as omission of contents may occur. In order to resolve these problems, previous researches have developed automated site monitoring method with the object recognition-based visualization or BIM data creation. Despite of the research results along with the related technology development, there are limitations in application targeting the practical construction projects due to the constraints in the experimental methods that assume the fixed equipment at a specific location. To overcome these limitations, some smart devices carried by the field workers can be employed as a medium for data creation. Specifically, the extracted information from the site picture by object recognition technology of CNN model, and positional information by GIPS are applied to update 4D BIM data. A standard CNN model is developed and BIM data modification experiments are conducted with the collected data to validate the research suggestion. Based on the experimental results, it is confirmed that the methods and performance are applicable to the construction site management and further it is expected to contribute speedy and precise data creation with the application of automated progress monitoring methods.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.3
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• pp.115-124
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• 2003

In this study, the relationships between the displacement and stress of the tunnel using various analysis methods were compared with monitoring results carried out during construction and maintenance monitoring. The behavior of tunnel were measured in the subway tunnel passing comparative soft the weathering and analyzed both security and mechanical characteristics of the tunnel lining. With the results of simplified monitoring observed in top heading and bench excavation tunnel, it is confirmed that the crown settlement is larger than the surface settlement. it is interesting to note that the crown settlement and the crown shotcrete lining stress are widely used monitoring items for the back analysis. It is analyzed that the residual water pressure applied in the drainage type tunnel is reasonable.