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.

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

• 정태영(㈜대한이앤씨 기업부설연구소) | Chung Tae-Young
• 박주경(㈜대한이앤씨) | Park Ju-Kyung