지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.31 No.4
- 발행연도
- 2023.4
- 수록면
- 311 - 323 (13page)
이용수
초록· 키워드
오류제보하기
Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based postearthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based postearthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.
목차
등록된 정보가 없습니다.
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Research on damage detection and assessment of civil engineering structures based on DeepLabV3+ deep learning model
Structural Engineering and Mechanics, An Int＇l Journal
2024 .09
-
Opendata 기반 포항 및 경주지진에 의한 건물손상 평가
한국지진공학회 논문집
2018 .01
-
Earthquake Disaster Simulation of Civil Infrastructures
대한토목학회지
2018 .02
-
세계유산의 지진 피해 대응 및 복원 과정 분석 - 네팔 카트만두 밸리를 중심으로 -
대한건축학회논문집
2024 .10
-
2016년 경주지진과 2011년 미국 버지니아지진에 대한 비교 연구 및 사례 분석
한국지진공학회 논문집
2016 .12
-
Development of a Targeted Recommendation Model for Earthquake Risk Prevention in the Whole Disaster Chain
JIPS(Journal of Information Processing Systems)
2021 .01
-
Earthquake Magnitudes for Certain South Korea Historical Earthquakes
대한토목학회 학술대회
2024 .10
-
Structural Seismic Design Optimization and Earthquake Engineering
대한토목학회지
2018 .01
-
역T형 옹벽의 지진시 거동특성Part II : 입력 지진파의 영향
한국지진공학회 논문집
2016 .01
-
포항 지진과 일본 지진관리 업무 비교·분석
한국지진공학회 논문집
2018 .01
-
Analysis of Damage Potential of the 912 Gyeongju Earthquake
Steel Construction
2017 .06
-
경주지진의 지진파 특성과 건축물 피해규모와의 상관관계 분석
대한건축학회 논문집 - 구조계
2016 .12
-
xShake: Intelligent wireless system for cost-effective real-time seismic monitoring of civil infrastructure
Smart Structures and Systems, An International Journal
2021 .10
-
Structural monitoring and identification of civil infrastructure in the United States
Structural monitoring and maintenance
2016 .01
-
포항지진에 의한 필로티 건축물 피해조사 및 피해원인 분석
대한건축학회 논문집 - 구조계
2019 .03
-
비선형동적해석에 의한 2016년 경주지진에서 지진피해를 받은 R/C 건물의 내진성능에 관한 연구
한국구조물진단유지관리공학회 논문집
2018 .01
-
지진의 규모와 거리에 따른 스펙트럼 형상과 다자유도 구조물에 대한 영향
한국지진공학회 논문집
2020 .01
-
국내 대규모 자연재해상황과 그 대책으로서의 방재설계
대한건축학회연합논문집
2017 .01
-
A review on deep learning-based structural health monitoring of civil infrastructures
Smart Structures and Systems, An International Journal
2019 .01
이 논문의 저자 정보
최근 본 자료
댓글(0)
0