This paper presents the effect on the inelastic behavior and structural performance of concrete and filled steel pipe through a numerical method for reliable judgment under various load conditions of the CJS composite structural system. Variable values optimized for the CJS synthetic structural system and the effects of multiple variables used for finite element analysis to present analytical modeling were compared and analyzed with experimental results. The Winfrith concrete model was used as a concrete material model that describes the confinement effect well, and the concrete structure was modeled with solid elements. Through geometric analysis of shell and solid elements, rectangular steel pipe columns and steel elements were modeled as shell elements. In addition, the slip behavior of the joint between the concrete column and the rectangular steel pipe was described using the Surface-to-Surface function. After finite element analysis modeling, simulation was performed for cyclic loading after assuming that the lower part of the foundation was a pin in the same way as in the experiment. The analysis model was verified by comparing the calculated analysis results with the experimental results, focusing on initial stiffness, maximum strength, and energy dissipation capability.

A B S T R A C T
1. 서 론
2. 유한요소해석 모델링
    2.1 콘크리트 재료 모델 결정
    2.2 각형강관 및 H보의 재료모델
    2.3 Contact 함수를 이용한 기둥 접착면 해석 모델 개발
    2.4 CJS 합성 보-기둥 접합부의 해석모델
3. 유한요소해석 수행과 결과 검증
    3.1 CJS-Int-BC 해석결과
    3.2 CJS-Int-RC 해석결과
    3.3 CJS-Int-RT 해석결과
    3.4 CJS-Ext-BC 해석결과
4. 요약 및 결론
REFERENCES

• 문아해(세종대학교 딥러닝건축연구소 건축공학과 석사과정) | Moon, A Hae (Graduate Student, Deep Learning Architecture Research Center, Dept. of Architectural Eng., Sejong University) Corresponding autho
• 신지욱(경상국립대학교 건축공학과 교수) | Shin, Jiuk (Professor, Dept. of Architectural Eng., Gyeongsang National University)
• 임창규(세종대학교 딥러닝건축연구소 건축공학과 석사과정) | Lim, Chang Gue (Graduate Student, Deep Learning Architecture Research Center, Dept. of Architectural Eng., Sejong University)
• 이기학(세종대학교 딥러닝건축연구소 건축공학과 교수) | Lee Kihak (Professor, Deep Learning Architecture Research Center, Dept. of Architectural Eng., Sejong University)