메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
Kim, Jaehong (University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering) LaFavet, James M. (University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering) Song, Junho (University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering)
저널정보
테크노프레스 Structural engineering and mechanics : An international journal Structural engineering and mechanics : An international journal 제27권 제4호
발행연도
2007.1
수록면
439 - 456 (18page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
Reinforced concrete (RC) joint shear strength models are constructed using an experimental database in conjunction with a Bayesian parameter estimation method. The experimental database consists of RC beam-column connection test subassemblies that maintained proper confinement within the joint panel. All included test subassemblies were subjected to quasi-static cyclic lateral loading and eventually experienced joint shear failure (either in conjunction with or without yielding of beam reinforcement); subassemblies with out-of-plane members and/or eccentricity between the beam(s) and the column are not included in this study. Three types of joint shear strength models are developed. The first model considers all possible influence parameters on joint shear strength. The second model contains those parameters left after a step-wise process that systematically identifies and removes the least important parameters affecting RC joint shear strength. The third model simplifies the second model for convenient application in practical design. All three models are unbiased and show similar levels of scatter. Finally, the improved performance of the simplified model for design is identified by comparison with the current ACI 352R-02 RC joint shear strength model.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0