Earticle

초록

영어

The purpose of this study is to present a model that can predict the appropriate level of input value, divided into 40MPa and 60MPa high fluidity concrete by applying neural network theory, confirm the mix design model through test validation, and is expected to minimize material waste and quality variation. Neural network theory was used as a method in this study. Algorithms for the neural network theory was selected as multilayer perceptrons and used back propagation algorithms to increase accuracy and reduce error rates while training. The factors in the neural network theory affect the compressive strength as input value, and collects the mixed design data through references related to the compressive strength characteristics. To validate the model, a compressive strength test was conducted based on the prediction of the mix design factor, comparing between the target compressive strength and the test value in mix design. According to the results of the mix design verification experiment, the lowest error rate in 40MPa high fluidity concrete was 4.3% composed of fly ash and silica fume and in 60MPa high fluidity concrete the lowest error rate was 5.8% composed of silica fume and ground granulated blast slag. Overall, 60MPa high fluidity concrete was higher than 40MPa high fluidity concrete.

목차

Abstract
1. 서론
1.1 연구배경 및 목적
1.2 연구내용 및 방법
1.3 기존 연구의 동향
2. 신경망 이론에 대한 예비적 고찰
2.1 정의
2.2 구성요소
3. 배합설계를 위한 모델링
3.1 신경망 모델링 방법
3.2 입출력 변수의 결정
3.3 자료 수집
3.4 배합설계를 위한 최적화
3.5 학습 검증
4. 모델검증
4.1 배합 인자 수준 예측
4.2 검증 실험
4.3 결과 및 분석
5. 결론
REFERENCES

키워드

저자

• 조성원 [ Cho, Sung-Won | 부산대학교 건축공학과 석사과정 ] Corresponding Author
• 조성은 [ Cho, Sung-Eun | 부산대학교 건축공학과 석사과정 ]
• 김영수 [ Kim, Young-Su | 부산대학교 건축공학과 교수, 공학박사 ]

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