• Title/Summary/Keyword: CNP Dongyang

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Application of Strcutral Health Monitoring in Structual Engineering for Buildings

  • Ji Young, Kim;Hobeom, Song;Kanghyun, Park;Kwangryang, Chung
    • International Journal of High-Rise Buildings
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    • v.11 no.3
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    • pp.221-226
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    • 2022
  • Installation of Structural Health Monitoring (SHM) system is a legal obligation for high-rise buildings over 200 m or 50-floor high in South Korea. CNP Dongyang has developed key technologies for SHM system design, installation, and data analyzing. Also, CNP Dongyang has applied SHM technology to a plenty of South Korea's representative high-rise buildings. The SHM technology, also, could be used in safety management of construction phase, evaluation of structural performance, etc. In this paper, state of the art SHM technologies and their application examples are introduced to give insight for future research and practical use of SHM.

Estimation of Displacements Using Artificial Intelligence Considering Spatial Correlation of Structural Shape (구조형상 공간상관을 고려한 인공지능 기반 변위 추정)

  • Seung-Hun Shin;Ji-Young Kim;Jong-Yeol Woo;Dae-Gun Kim;Tae-Seok Jin
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.36 no.1
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    • pp.1-7
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    • 2023
  • An artificial intelligence (AI) method based on image deep learning is proposed to predict the entire displacement shape of a structure using the feature of partial displacements. The performance of the method was investigated through a structural test of a steel frame. An image-to-image regression (I2IR) training method was developed based on the U-Net layer for image recognition. In the I2IR method, the U-Net is modified to generate images of entire displacement shapes when images of partial displacement shapes of structures are input to the AI network. Furthermore, the training of displacements combined with the location feature was developed so that nodal displacement values with corresponding nodal coordinates could be used in AI training. The proposed training methods can consider correlations between nodal displacements in 3D space, and the accuracy of displacement predictions is improved compared with artificial neural network training methods. Displacements of the steel frame were predicted during the structural tests using the proposed methods and compared with 3D scanning data of displacement shapes. The results show that the proposed AI prediction properly follows the measured displacements using 3D scanning.