Earticle

초록

한국어

The purpose of this study is to suggest 100MPa class ultra high strength concrete mix design model applying neural network theory, in order to minimize an effort wasted by trials and errors method until now. Mix design model was applied to each of the 70 data using binary binder, ternary binder and quaternary binder. Then being repeatedly applied to back-propagation algorithm in neural network model, optimized connection weight was gained. The completed mix design model was proved, by analyzing and comparing to value predicted from mix design model and value measured from actual compressive strength test. According to the results of this study, more accurate value could be gained through the mix design model, if error rate decreases with the test condition and environment. Also if content of water and binder, slump flow, and air content of concrete apply to mix design model, more accurate and resonable mix design could be gained.

목차

Abstract
1. 서론
1.1 연구배경 및 목적
1.2 연구범위 및 방법
2. 초고강도 배합설계 시험
2.1 사전 배합실험 계획
2.2 사용재료
2.3 시험결과
3. 배합설계를 위한 모델링
3.1 신경망 모델링 방법
3.2 입력변수 및 출력변수의 결정
3.3 배합설계모델 최적화
3.4 학습검증
4. 모델 검증
4.1 배합 인자 수준 예측
4.2 검증실험
4.3 결과 분석
5. 결론
REFERENCES

키워드

저자

• 김영수 [ Kim, Young-Soo | 부산대학교 건축공학과 교수 공학박사 ]
• 신상엽 [ Shin, Sang-Yeop | ING&ENG, 연구실장, 공학박사 ]
• 정의창 [ Jeong, Euy-Chang | ING&ENG, 기획실장, 공학박사 ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한건축학회연합논문집 [Journal of the Regional Association of Architectural Institute of Korea]

• 간기

  격월간

• pISSN

  1229-5752

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 540 DDC 690