Smart Structures and Systems

  • 제33권4호
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  • Pages.313-323
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  • 2024
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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A deep neural network to automatically calculate the safety grade of a deteriorating building

  • Seungho Kim (Department of Architecture, Yeungnam University College) ;
  • Jae-Min Lee (School of Architecture, Yeungnam University) ;
  • Moonyoung Choi (School of Architecture, Yeungnam University) ;
  • Sangyong Kim (School of Architecture, Yeungnam University)
  • 투고 : 2022.05.20
  • 심사 : 2024.04.26
  • 발행 : 2024.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Deterioration of buildings is one of the biggest problems in modern society, and the importance of a safety diagnosis for old buildings is increasing. Therefore, most countries have legal maintenance and safety diagnosis regulations. However, the reliability of the existing safety diagnostic processes is reduced because they involve subjective judgments in the data collection. In addition, unstructured tasks increase rework rates, which are time-consuming and not cost-effective. Therefore, This paper proposed the method that can calculate the safety grade of deterioration automatically. For this, a DNN structure is generated by using existing precision inspection data and precision safety diagnostic data, and an objective building safety grade is calculated by applying status evaluation data obtained with a UAV, a laser scanner, and reverse engineering 3D models. This automated process is applied to 20 old buildings, taking about 40% less time than needed for a safety diagnosis from the existing manual operation based on the same building area. Subsequently, this study compares the resulting value for the safety grade with the already existing value to verify the accuracy of the grade calculation process, constructing the DNN with high accuracy at about 90%. This is expected to improve the reliability of aging buildings in the future, saving money and time compared to existing technologies, improving economic efficiency.

키워드

과제정보

This work was supported by the 2022 Yeungnam University Research Grant.

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