신경망 이론과 Mahalanobis Distance 이상치 탐색방법을 이용한 고강도 콘크리트 강도 예측 모델 개발에 관한 연구

• 영문명: Modeling of Strength of High Performance Concrete with Artificial Neural Network and Mahalanobis Distance Outlier Detection Method
• 발행기관: 한국산업경영시스템학회
• 저자명: 홍정의(Jungeui Hong)
• 간행물 정보: 『한국산업경영시스템학회 학술대회』2010년 추계학술대회 논문집, 1~8쪽, 전체 8쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2010.11.30

영문 초록

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.

목차

1. 서론

2. Mahalanobis Distance

3. MD를 이용한 이상치의 제거

4. 신경망을 이용한 고강도 콘크리트 강도 예측 모델

5. HPC 강도와 변수와의 관계

6. 결론

키워드

Mahalanobis Distance Neural Network Multiple Regression