High-performance concrete(HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance(MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction perfomance.

Abstract
 1. 서론
 2. Mahalanobis Distance
 3. MD를 이용한 이상치의 제거
  3-1. 변수 데이터
  3-2. 이상치 제거
 4. 신경망을 이용한 고강도 콘크리트 강도 예측 모델
 5. HPC 강도와 변수와의 관계
 6.. 결론
 참고문헌

• 홍정의(충주대학교 공과대학 산업경영공학과)