• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.87-88
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• 2022

For an earthquake-safe urban environment, the Republic of Korea conducts seismic performance evaluation in accordance with laws and guidelines to assign safety ratings and implement necessary management measures such as repairs and reinforcements. In the seismic performance evaluation result, structures lacking in preparation for earthquakes are prioritized and classified into measures such as repair, reinforcement, or careful observation to respond to physical risks such as earthquakes. Such repair and reinforcement work is not a one-time thing, but it is necessary to further enhance the effect through continuous follow-up observation. In this study, the location of the vertical and horizontal displacement measuring part of the construction part is displayed so that the post-construction status of the reinforcement construction part can be visually checked by identifying the problems in the process of post-monitoring in 2022 for the maintenance and reinforcement work of local governments' public facilities carried out in 2021. We propose a plan to institutionalize the installation of, inspection tools, and crack gauges at certain locations in the construction department, and to have facility managers periodically inspect and manage them with a smartphone program or the 'Facility Autonomous Safety Inspection' app.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.43-51
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• 2018

As the inventory of aged apartments is expected to increase explosively, the importance of maintenance to improve the durability of concrete facilities is increasing. Concrete compressive strength is a representative index of durability of concrete facilities, and is an important item in the precision safety diagnosis for facility maintenance. However, existing methods for measuring the concrete compressive strength and determining the maintenance of concrete facilities have limitations such as facility safety problem, high cost problem, and low reliability problem. In this study, we proposed a model that can predict the concrete compressive strength through images by using deep convolution neural network technique. Learning, validation and testing were conducted by applying the concrete compressive strength dataset constructed through the concrete specimen which is produced in the laboratory environment. As a result, it was found that the concrete compressive strength could be learned by using the images, and the validity of the proposed model was confirmed.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.30-38
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• 2021

In this paper, we propose a method for extracting thermal data from thermal image and improving detection of heating equipment using the data. The main goal is to read the data in bytes from the thermal image file to extract the thermal data and the real image, and to apply the composite image obtained by synthesizing the image and data to the deep learning model to improve the detection accuracy of the heating facility. Data of KHNP was used for evaluation data, and Faster-RCNN is used as a learning model to compare and evaluate deep learning detection performance according to each data group. The proposed method improved on average by 0.17 compared to the existing method in average precision evaluation.As a result, this study attempted to combine national data-based thermal image data and deep learning detection to improve effective data utilization.

• Journal of radiological science and technology
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• v.41 no.4
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• pp.351-360
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• 2018

In recent years, demand for examination of bone mineral density (BMD) is increasing in Korea according aging society. Therefore, it is required to develop an efficient management program that can increase the safety and reliability of Dual Energy X-ray Absorptiometry (DXA) that can be applied to the criteria of the World Health Organization. It is necessary to develop a management program that can design a program to improve the accuracy and precision of the results of the analysis and to improve the accuracy of diagnosis of osteoporosis by development a high quality DXA report. It is recommended to prepare the examination manuals and to establish procedures of standard operating including the program to prevent the pitfalls during the examination, the compatibility evaluation of the examination data, and the contents of the radiation safety. In addition, relevant regulations on the production of high-quality DXA reports are required and government and related agencies should introduce individual and facility recognition programs through DXA measurement and education programs and training. It is considered that efforts should be made to prepare high quality DXA report by guidelines on all aspects of BMD for preparation about aging society.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.66-71
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• 2019

In this paper, a study was conducted on the method of using GPR data to predict rebar thickness inside a facility. As shown in the cases of poor construction, such as the use of rebars below the domestic standard and the construction of reinforcement, information on rebar thickness can be found to be essential for precision safety diagnosis of structures. For this purpose, the B-scan data of GPR was obtained by gradually increasing the diameter of rebars by making specimen. Because the B-scan data of GPR is less visible, the data was converted into the heatmap image data through migration to increase the intuition of the data. In order to compare the results of application of commonly used B-scan data and heatmap data to CNN, this study extracted areas for rebars from B-scan and heatmap data respectively to build training and validation data, and applied CNN to the deployed data. As a result, better results were obtained for the heatmap data when compared with the B-scan data. This confirms that if GPR heatmap data are used, rebar thickness can be predicted with higher accuracy than when B-scan data is used, and the possibility of predicting rebar thickness inside a facility is verified.