RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      KCI등재

      Information-Based Hybrid Modeling Framework on the Systematic use of Artificial Neural-Networks = 구조모델 개선을 위한 정보기반 하이브리드 모델링 기법

      한글로보기

      https://www.riss.kr/link?id=A101121871

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual Abstract)

      In this study, a new information-based hybrid modeling framework is proposed. In the hybrid framework, a conventional mathematical model is complemented by the informational methods. The basic premise of the proposed hybrid methodology is that not all features of system response are amenable to mathematical modeling, hence considering informational alternatives. This may be because (i) the underlying theory is not available or not sufficiently developed, or (ii) the existing theory is too complex and therefore not suitable for modeling within building frame analysis. The role of informational methods is to model aspects that the mathematical model leaves out. Autoprogressive algorithm and self-learning simulation extract the missing aspects from a system response. In a hybrid framework, experimental data is an integral part of modeling, rather than being used strictly for validation processes. The potential of the hybrid methodology is illustrated through modeling complex hysteretic behavior of beam-to-column connections.
      번역하기

      In this study, a new information-based hybrid modeling framework is proposed. In the hybrid framework, a conventional mathematical model is complemented by the informational methods. The basic premise of the proposed hybrid methodology is that not all...

      In this study, a new information-based hybrid modeling framework is proposed. In the hybrid framework, a conventional mathematical model is complemented by the informational methods. The basic premise of the proposed hybrid methodology is that not all features of system response are amenable to mathematical modeling, hence considering informational alternatives. This may be because (i) the underlying theory is not available or not sufficiently developed, or (ii) the existing theory is too complex and therefore not suitable for modeling within building frame analysis. The role of informational methods is to model aspects that the mathematical model leaves out. Autoprogressive algorithm and self-learning simulation extract the missing aspects from a system response. In a hybrid framework, experimental data is an integral part of modeling, rather than being used strictly for validation processes. The potential of the hybrid methodology is illustrated through modeling complex hysteretic behavior of beam-to-column connections.

      더보기

      참고문헌 (Reference)

      1 Hashash, Y.M.A., "Systematic Update of a Deep Excavation Model Using Field Performance Data" 30 (30): 477-488, 2003

      2 Shin, H.S., "On self-learning Finite Element Codes Based on Monitored Response of Structures" 27 (27): 161-178, 2000

      3 Hashash, Y.M.A, "Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations" 132 (132): 1019-1031, 2006

      4 Ghaboussi J., "New Nested Adaptive Neural Networks (NANN) for Constitutive Modeling" 22 (22): 29-52, 1998

      5 Ghaboussi J., "Nested Adaptive Neural Network: A NewArchitecture" 1997

      6 Kim, J.H., "Mechanical and Informational Modeling of Steel Beam-to-Column Connections" 32 (32): 449-458, 2010

      7 Ghaboussi, J., "Material Modeling with Neural Networks" 701-717, 1990

      8 Ghaboussi J., "Knowledge-based Modeling of Material Behavior with Neural Networks" 117 (117): 132-153, 1991

      9 Bernuzzi, C., "Experimental Analysis and Modelling of Semi-Rigid Steel Joints under Cyclic Reversal Loading" 38 (38): 95-123, 1996

      10 Ghaboussi, J., "Autoprogressive Training of Neural Network Constitutive Models" 42 (42): 105-126, 1998

      1 Hashash, Y.M.A., "Systematic Update of a Deep Excavation Model Using Field Performance Data" 30 (30): 477-488, 2003

      2 Shin, H.S., "On self-learning Finite Element Codes Based on Monitored Response of Structures" 27 (27): 161-178, 2000

      3 Hashash, Y.M.A, "Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations" 132 (132): 1019-1031, 2006

      4 Ghaboussi J., "New Nested Adaptive Neural Networks (NANN) for Constitutive Modeling" 22 (22): 29-52, 1998

      5 Ghaboussi J., "Nested Adaptive Neural Network: A NewArchitecture" 1997

      6 Kim, J.H., "Mechanical and Informational Modeling of Steel Beam-to-Column Connections" 32 (32): 449-458, 2010

      7 Ghaboussi, J., "Material Modeling with Neural Networks" 701-717, 1990

      8 Ghaboussi J., "Knowledge-based Modeling of Material Behavior with Neural Networks" 117 (117): 132-153, 1991

      9 Bernuzzi, C., "Experimental Analysis and Modelling of Semi-Rigid Steel Joints under Cyclic Reversal Loading" 38 (38): 95-123, 1996

      10 Ghaboussi, J., "Autoprogressive Training of Neural Network Constitutive Models" 42 (42): 105-126, 1998

      더보기

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      인용정보 인용지수 설명보기

      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2028 평가예정 재인증평가 신청대상 (재인증)
      2022-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2019-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2016-01-01 평가 등재학술지 선정 (계속평가) KCI등재
      2015-12-01 평가 등재후보로 하락 (기타) KCI등재후보
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-05-29 학술지명변경 외국어명 : 미등록 -> Journal of the Computational Structural Engineering Institute of Korea KCI등재
      2005-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2002-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      더보기

      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.27 0.27 0.23
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.22 0.2 0.443 0.03
      더보기

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼