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학술논문대한안전경영과학회지2021.12 발행

공기조화설비 겸용 제연설비 덕트의 성능개선을 위한 연구

A Study on Performance Improvements about Duct of Smoke Control System Combined with Air-Conditioning Equipment

오택흠(신화 FEC); 박찬석(서원대학교)

23권 4호, 67~72쪽

초록

To ensure the safety and functionality of a railroad bridge, maintaining the integrity of the bridge via continuous structural health monitoring is important. However, most structural integrity monitoring methods proposed to date are based on modal responses which require the extracting process and have limited availability. In this paper, the applicability of the existing damage identification method based on free-vibration reponses to time-domain deflection shapes due to moving train load is investigated. Since the proposed method directly utilizes the time-domain responses of the structure due to the moving vehicles, the extracting process for modal responses can be avoided, and the applicability of structural health evaluation can be enhanced. The feasibility of the presented method is verified via a numerical example of a simple plate girder bridge.

Abstract

To ensure the safety and functionality of a railroad bridge, maintaining the integrity of the bridge via continuous structural health monitoring is important. However, most structural integrity monitoring methods proposed to date are based on modal responses which require the extracting process and have limited availability. In this paper, the applicability of the existing damage identification method based on free-vibration reponses to time-domain deflection shapes due to moving train load is investigated. Since the proposed method directly utilizes the time-domain responses of the structure due to the moving vehicles, the extracting process for modal responses can be avoided, and the applicability of structural health evaluation can be enhanced. The feasibility of the presented method is verified via a numerical example of a simple plate girder bridge.

발행기관:
대한안전경영과학회
분류:
안전공학

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공기조화설비 겸용 제연설비 덕트의 성능개선을 위한 연구 | 대한안전경영과학회지 2021 | AskLaw | 애스크로 AI