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User-friendly end-to-end fiber identification for fiber-reinforced cementitious composites (FRCC) through deep learning
https://orcid.org/0000-0002-9228-4711
Highlights Presenting a general and user-friendly end-to-end fiber identification method for FRCC through deep learning. Accomplishing accurate pixel-wise segmentation of fibers without introducing noises. Investigating the impact of pores on fiber identification in cross-sections by comparing 2-class and 3-class models. Visualizing the extracted multi-dimensional features to gain insights into the underlying deep learning mechanisms. Conducting case study on fiber distribution and orientation and comparing the proposed method with CT.
Abstract Authentic fiber distribution and orientation in fiber-reinforced cementitious composites (FRCC) is vital in study of cracking mechanism and tensile mechanics model. Existing analytical methods based on fluorescent microscopes or CT are complex, time-consuming and labor-intensive. This study presents a general and user-friendly end-to-end fiber identification method for FRCC through deep learning, which accomplished greatly accurate semantic segmentation of fibers (>100 μm) with an ordinary SLR camera. The optimal model achieved MIoU of 98.93% and class accuracy of 99.998% for fiber. Specifically, the effect of pore, which is the possible interference to fiber identification in the cross-sections, was analyzed and the extracted multidimensional features were visualized and discussed. Furthermore, a case study on fiber distribution and orientation based on the proposed method was carried out. The results show that new advances are made in terms of the simplicity of modeling process, the accuracy and intuitiveness of results, and the user experience.
User-friendly end-to-end fiber identification for fiber-reinforced cementitious composites (FRCC) through deep learning
https://orcid.org/0000-0002-9228-4711
Highlights Presenting a general and user-friendly end-to-end fiber identification method for FRCC through deep learning. Accomplishing accurate pixel-wise segmentation of fibers without introducing noises. Investigating the impact of pores on fiber identification in cross-sections by comparing 2-class and 3-class models. Visualizing the extracted multi-dimensional features to gain insights into the underlying deep learning mechanisms. Conducting case study on fiber distribution and orientation and comparing the proposed method with CT.
Abstract Authentic fiber distribution and orientation in fiber-reinforced cementitious composites (FRCC) is vital in study of cracking mechanism and tensile mechanics model. Existing analytical methods based on fluorescent microscopes or CT are complex, time-consuming and labor-intensive. This study presents a general and user-friendly end-to-end fiber identification method for FRCC through deep learning, which accomplished greatly accurate semantic segmentation of fibers (>100 μm) with an ordinary SLR camera. The optimal model achieved MIoU of 98.93% and class accuracy of 99.998% for fiber. Specifically, the effect of pore, which is the possible interference to fiber identification in the cross-sections, was analyzed and the extracted multidimensional features were visualized and discussed. Furthermore, a case study on fiber distribution and orientation based on the proposed method was carried out. The results show that new advances are made in terms of the simplicity of modeling process, the accuracy and intuitiveness of results, and the user experience.
User-friendly end-to-end fiber identification for fiber-reinforced cementitious composites (FRCC) through deep learning
https://orcid.org/0000-0002-9228-4711
Highlights Presenting a general and user-friendly end-to-end fiber identification method for FRCC through deep learning. Accomplishing accurate pixel-wise segmentation of fibers without introducing noises. Investigating the impact of pores on fiber identification in cross-sections by comparing 2-class and 3-class models. Visualizing the extracted multi-dimensional features to gain insights into the underlying deep learning mechanisms. Conducting case study on fiber distribution and orientation and comparing the proposed method with CT.
Abstract Authentic fiber distribution and orientation in fiber-reinforced cementitious composites (FRCC) is vital in study of cracking mechanism and tensile mechanics model. Existing analytical methods based on fluorescent microscopes or CT are complex, time-consuming and labor-intensive. This study presents a general and user-friendly end-to-end fiber identification method for FRCC through deep learning, which accomplished greatly accurate semantic segmentation of fibers (>100 μm) with an ordinary SLR camera. The optimal model achieved MIoU of 98.93% and class accuracy of 99.998% for fiber. Specifically, the effect of pore, which is the possible interference to fiber identification in the cross-sections, was analyzed and the extracted multidimensional features were visualized and discussed. Furthermore, a case study on fiber distribution and orientation based on the proposed method was carried out. The results show that new advances are made in terms of the simplicity of modeling process, the accuracy and intuitiveness of results, and the user experience.
User-friendly end-to-end fiber identification for fiber-reinforced cementitious composites (FRCC) through deep learning
Hao, Zhexin (author) / Lu, Cong (author)
2023-08-28
Article (Journal)
Electronic Resource
English
Shear Crack Formation and Propagation in Fiber Reinforced Cementitious Composites (FRCC)
| Springer Verlag | 2012
| Taylor & Francis Verlag | 2018