http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : George Papazafeiropoulos (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Behavior of composite CFST beam-steel column joints
엄순섭,Quang-Viet Vu,최지훈,George Papazafeiropoulos,김승억 국제구조공학회 2019 Steel and Composite Structures, An International J Vol.32 No.5
In recent years, composite concrete-filled steel tubular (CFST) members have been widely utilized in framed building structures like beams, columns, and beam-columns since they have significant advantages such as reducing construction time, improving the seismic performance, and possessing high ductility, strength, and energy absorbing capacity. This paper presents a new composite joint - the composite CFST beam-column joint in which the CFST member is used as the beam. The main components of the proposed composite joint are steel H-beams, CFST beams welded with the steel H-column, and a reinforced concrete slab. The steel H-beams and CFST beams are connected with the concrete slab using shear connectors to ensure composite action between them. The structural performance of the proposed composite joint was evaluated through an experimental investigation. A three-dimensional (3D) finite element (FE) model was developed to simulate this composite joint using the ABAQUS/Explicit software, and the accuracy of the FE model was verified with the relevant experimental results. In addition, a number of parametric studies were made to examine the effects of the steel box beam thickness, concrete compressive strength, steel yield strength, and reinforcement ratio in the concrete slab on the proposed joint performance.
-
Seung-Eock Kim,Quang-Viet Vu,George Papazafeiropoulos,Zhengyi Kong,Viet-Hung Truong 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.37 No.2
In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
