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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.21 No.4
- 발행연도
- 2018.1
- 수록면
- 421 - 433 (13page)
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초록· 키워드
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Carbon fiber reinforced polymer (CFRP) cable has good mechanical properties and corrosion resistance. However, the anchorage of CFRP cable is a big issue due to the anisotropic property of CFRP material. In this article, a high-efficient bonding anchorage with novel configuration is developed for CFRP cables. The acoustic emission (AE) technique is employed to evaluate the performance of anchorage in the fatigue test and post-fatigue ultimate bearing capacity test. The obtained AE signals are analyzed by using a combination of unsupervised K-means clustering and supervised K-nearest neighbor classification (K-NN) for quantifying the performance of the anchorage and damage evolutions. An AE feature vector (including both frequency and energy characteristics of AE signal) for clustering analysis is proposed and the under-sampling approaches are employed to regress the influence of the imbalanced classes distribution in AE dataset for improving clustering quality. The results indicate that four classes exist in AE dataset, which correspond to the shear deformation of potting compound, matrix cracking, fiber-matrix debonding and fiber fracture in CFRP bars. The AE intensity released by the deformation of potting compound is very slight during the whole loading process and no obvious premature damage observed in CFRP bars aroused by anchorage effect at relative low stress level, indicating the anchorage configuration in this study is reliable.
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참고문헌 (25)
참고문헌 신청
Abdi, H. / 2010 / Principal component analysis / Wiley Interdisciplinary Reviews: Computational Statistics 2(4):433~459
Alrabea, A. / 2013 / Enhancing K-means algorithm with initial cluster centers derived from data partitioning along the data axis with PCA / J. Adv. Comput. Netw. 1(2):137~142
de Groot, P.J. / 1995 / Real-time frequency determination of acoustic emission for different fracture mechanisms in carbon/epoxy composites / Compos. Sci. Technol. 55(4):405~412
Ding, C. / 2004 / K-means clustering via principal component analysis / Proceedings of the International Conference on Machine Learning
Drummond, C. / 2003 / C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling / Workshop on learning from imbalanced datasets
Alrabea, A. / 2013 / Enhancing K-means algorithm with initial cluster centers derived from data partitioning along the data axis with PCA / J. Adv. Comput. Netw. 1(2):137~142
de Groot, P.J. / 1995 / Real-time frequency determination of acoustic emission for different fracture mechanisms in carbon/epoxy composites / Compos. Sci. Technol. 55(4):405~412
Ding, C. / 2004 / K-means clustering via principal component analysis / Proceedings of the International Conference on Machine Learning
Drummond, C. / 2003 / C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling / Workshop on learning from imbalanced datasets
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