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
Liu, Shuqian (Department of Civil and Environmental Engineering, Louisiana State University) Cai, C.S. (Department of Civil and Environmental Engineering, Louisiana State University) Han, Yan (School of Civil Engineering, Changsha University of Science & Technology) Li, Chunguang (School of Civil Engineering, Changsha University of Science & Technology)
저널정보
테크노프레스 Wind & structures Wind & structures 제27권 제3호
발행연도
2018.1
수록면
175 - 186 (12page)

이용수

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

초록· 키워드

오류제보하기
With the continuous increase of span lengths, modern bridges are becoming much more flexible and more prone to flutter under wind excitations. A reasonable probabilistic flutter analysis of long-span bridges involving random and uncertain variables may have to be taken into consideration. This paper presents a method for estimating the reliability index and failure probability due to flutter, which considers the very important variables including the extreme wind velocity at bridge site, damping ratio, mathematical modeling, and flutter derivatives. The Aizhai Bridge in China is selected as an example to demonstrate the numerical procedure for the flutter reliability analysis. In the presented method, the joint probability density function of wind speed and wind direction at the deck level of the bridge is first established. Then, based on the fundamental theories of structural reliability, the reliability index and failure probability due to flutter of the Aizhai Bridge is investigated by applying the Monte Carlo method and the first order reliability method (FORM). The probabilistic flutter analysis can provide a guideline in the design of long-span bridges and the results show that the structural damping and flutter derivatives have significant effects on the flutter reliability, more accurate and reliable data of which is needed.

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