Journal of the Korea Concrete Institute (콘크리트학회논문집)

Korea Concrete Institute (한국콘크리트학회)
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Fiber Classification and Detection Technique Proposed for Applying on the PVA-ECC Sectional Image

PVA-ECC단면 이미지의 섬유 분류 및 검출 기법

  • Kim, Yun-Yong (Dept. of Civil Engineering, Chungnam National University) ;
  • Lee, Bang-Yeon (Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Kim, Jin-Keun (Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology)
  • 김윤용 (충남대학교 토목공학과) ;
  • 이방연 (한국과학기술원 건설 및 환경공학과) ;
  • 김진근 (한국과학기술원 건설 및 환경공학과)
  • Published : 2008.08.31
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Abstract

The fiber dispersion performance in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion performance in the composite PVA-ECC (Polyvinyl alcohol-Engineered Cementitious Composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, an enhanced fiber detection technique is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a Charged Couple Device (CCD) camera through a microscope. The fibers are more accurately detected by employing a series of process based on a categorization, watershed segmentation, and morphological reconstruction.

섬유복합재료의 우수한 인장 성능은 섬유가 매트릭스의 균열 면에서 가교작용을 함으로써 발현되기 때문에 섬유의 분포 특성이 복합재료의 성능에 결정적인 영향을 미치게 된다. 그러나 PVA 섬유를 보강 섬유로 사용하는 섬유복합재료의 경우 PVA 섬유와 매트릭스 사이의 낮은 명암비와 PVA의 비전도성 특징으로 인하여 섬유의 위치 및 분포특성을 정량적으로 평가히는 방법은 연구가 미흡한 실정이다. 이 연구에서는 PVA 섬유를 보강 섬유로 사용하는 섬유복합재료의 섬유 분포 특성 등을 평가할 때 가장 중요한 과정인 섬유의 검출에 대하여 검출 성능을 향상 시킬 수 있는 알고리즘을 제시하였다. 제안한 알고리즘은 형광 현미경을 사용하여 얻은 섬유 이미지를 유형별로 분류하고, 분류된 분류된 섬유 이미지의 특성에 따라 분수령 알고리즘 (watershed algorithm)과 형태학적 재구성 (morphological reconstruction)을 이용하여 보다 정확히 섬유를 검출하는 과정으로 구성된다. 이 과정에서 섬유 이미지를 총 5가지 유형으로 분류하였으며, 인공신경회로망(ANN)을 분류기로 활용하기 위하여 형상 특성을 나타내는 5가지 특징값 즉, $F_s$, $F_c$, $F_p$, $F_l$과 $F_{rl}$을 추출하였다. 추출된 특징값에 대한 데이터베이스를 구축하여 ANN을 학습하여 분류기를 구축함으로써 섬유의 유형을 자동으로 분류할 수 있도록 하였다. 또한 5가지 섬유 이미지 유형 중에서 잘못 검출된 섬유이미지를 분수령 알고리즘과 형태학적 재구성을 통하여 섬유를 정확히 검출할 수 있는 기법을 제안하였다.

Keywords

References

  1. Li, V. C., Wang, S., and Wu, C., "Tensile Strain- Hardening Behavior of Polyvinyl Alcohol-Engineered Cementitious Composite (PVA-ECC)," ACI Materials Journal, Vol. 98, No. 6, 2001, pp. 483-492
  2. Guild, F. J. and Summerscales, J., "Microstructural Image Analysis Applied to Fibre Composites Materials: A Review," Composites, Vol. 24, No. 5, 1993, pp. 383-393 https://doi.org/10.1016/0010-4361(93)90246-5
  3. Yang, Y., "Methods Study on Dispersion of Fibers in CFRC," Cement and Concrete Research, Vol. 32, 2002, pp. 747-750 https://doi.org/10.1016/S0008-8846(01)00759-1
  4. Chermant, J. L., Chermant, L., Coster, M., Dequiedt, A. S., and Redon, C., "Some Fields of Applications of Automatic Image Analysis in Civil Engineering," Cement and Concrete Composites, Vol. 23, 2001, pp. 157-169 https://doi.org/10.1016/S0958-9465(00)00059-7
  5. Benson, S. D. P. and Karihaloo, B. L., "CARDIFRCo- Manufacture and Constitutive Behavior," High Performance Fiber Reinforced Cement Composites (HPFRCC4), Ann Arbor, Mich. 2003, pp. 65-79
  6. Akkaya, Y., Shah, S. P. and Ankenman, B., "Effect of Fiber Dispersion on Multiple Cracking of Cement Composites," Journal of Engineering Materials in Civil Engineering, Vol. 127, No. 4, 2001, pp. 311-316
  7. Ozyurt, N., Woo, L. Y, Mason, T. O., and Shah, S. P., "Monitoring Fiber Dispersion in Fiber-Reinforced Cementitious Materials: Comparison of AC-Impedance Spectroscopy and Image Analysis," ACI Materials Journal, Vol. 103, No. 5, 2006, pp. 340-347
  8. 김윤용, 이방연, 김정수, 김진근, "이미지 프로세싱 기법을 이용한 섬유복합재료의 정량적인 섬유분산성 평가," 비파괴검사학회지, 27권, 2호, 2007, pp. 148-156
  9. Torigoe, S., Horikoshi, T., and Ogawa, A., "Study on Evaluation Method for PVA Fiber Distribution in Engineered Cementitious Composite," Journal of Advanced Concrete Technology, Vol. 1, No. 3, 2003, pp. 265-268 https://doi.org/10.3151/jact.1.265
  10. Otsu, N. A., "Threshold Selection Method from Gray Level Histogram," IEEE Transactions on Systems, Man, and Cybernetics, SMC, Vol. 9, No. 1, 1979, pp. 62-66 https://doi.org/10.1109/TSMC.1979.4310076
  11. Vincent, L. and Soille, P., "Watesheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, 1991, pp. 583-598 https://doi.org/10.1109/34.87344
  12. Beucher, S., "The Watershed Transformation Applied to Image Segmentation," Conference on Signal and Image Processing in Microscopy and Microanalysis, September, 1991, pp. 299-314
  13. Barber, C. B., Dobkin, D. P., and Huhdanpaa, H. T., "The Quickhull Algorithm for Convex Hulls," ACM Transactions on Mathematical Software, Vol. 22, No. 4, 1996, pp. 469-483 https://doi.org/10.1145/235815.235821
  14. Moody, J. E. and Yarvin, N., "Networks with Learned unit Response Functions," In: Moody, J. E., Hanson, S. J., Lippmann, R. P., Editors. Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, 1992, pp. 1048-1055
  15. Hecht-Nielsen, R., "Theory of the Backpropogation Neural Network," In Proceedings of International Joint Conference on Neural Networks, Washington, D.C., USA: IEEE, V. I, 1989, pp. 593-605
  16. Barron, A. R., "Universal Approximation Bounds for Superpositions of a Sigmoidal Function," IEEE Transactions of Information Theory, Vol. 39, No. 3, 1993, pp. 930-945 https://doi.org/10.1109/18.256500
  17. Krogh, A. and Hertz, J. A., "A Simple Weight Decay Can Improve Generalization," In: Moody, J. E., Hanson, S. J., Lippmann, R. P., Editors. Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, 1992, pp. 950-957
  18. Digabel, H. and Lantu`ejoul, C. "Iterative algorithms, in Proc. 2nd European Symp. Quantitative Analysis of Microstructures in Material Science, Biology and Medicine," Caen, France, Oct. 1977, Chermant, J. L. (Ed.) Stuttgart, West Germany, Riederer Verlag, 1978, pp. 85-99
  19. Beucher, S. and Lantue`joul, C., "Use of Watershed in Contour Detection," in International Workshop on Image Processing: Real-time edge and motion detection/estimation. Rennes, France, 1979, pp. 17-21
  20. Feng, J. and Lu, H., "Peak Analysis of Grayscale Image: Algorithm and Application," International Journal of Information Technology, Vol. 12, No. 5, 2006, pp. 11-18
  21. Vincent, L., "Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms," IEEE Transactions on Image Processing, Vol. 2, No. 2, 1993, pp. 176-201 https://doi.org/10.1109/83.217222