복합신소재구조학회 학술발표회 2018년 (제8회) 한국복한신소재구조학회 학술발표회 논문집 (p.111-114)

신경망 기법을 이용한 강섬유 혼입율, 형상비에 따른 압축강도 추정모형 개발

Development of Estimated Model for Compressive Strength according Volume of Fraction and Aspect Ratio of Steel Fiber Reinforced Concrete Using Neural Networks
키워드 :
SFRC,nerve network

목차

ABSTRACT
1. 서 론
2. 신경망 모형의 기본 이론
  2.1 신경망 모형의 개념
  2.2 신경망 모형의 종류
  2.3 신경망 모형의 장점
  2.4 신경망 모형의 단점
3. SFRC의 압축강도 추정 모형 개발
  3.1 개요
  3.2 통계적 자료 수집 및 분석
  3.3 압축강도 추정 모형 개발
4. 개발된 모형 검증
  4.1 개요
  4.2 압축강도 추정 모형 검증
5. 결론
References

초록

The contemporary high-tech structures have become enlarged and their functions more diversified. Steel concrete structure and composite material structures are not exceptions. Therefore, there have been on-going studies on fiber reinforcement materials to improve the characteristics of brittleness, bending and tension stress and others, the short-comings of existing concrete. In this study, the purpose is to develop the estimated model with dynamic characteristics following the steel fiber mixture rate and formation ration by using the nerve network in mixed steel fiber reinforced concrete (SFRC). This study took a look at the tendency of studies by collecting and analyzing the data of the advanced studies on SFRC, and facilitated it on the learning data required in the model development. In addition, by applying the diverse nerve network model and various algorithms to develop the optimal nerve network model appropriate to the dynamic characteristics. The accuracy of the developed nerve network model was compared with the experiment data value of other researchers not utilized as the learning data, the experiment data value undertaken in this study, and comparison made with the formulas proposed by the researchers. And, by analyzing the influence of learning data of nerve network model on the estimation result, the sensitivity of the forecasting system on the learning data of the nerve network is analyzed.