Synergistic approach to quantifying information on a crack-based network in loess/water material composites using deep learning and network science
[Display omitted] •Deep learning and network science are synergistically applied to the crack networks.•Network science is employed to quantify the interconnected features between nodes.•CNN and YOLO algorithms are proven to be effective in detecting the nodes and edges. Deep learning and network sc...
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| Published in: | Computational materials science Vol. 166; pp. 240 - 250 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01-08-2019
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| Abstract | [Display omitted]
•Deep learning and network science are synergistically applied to the crack networks.•Network science is employed to quantify the interconnected features between nodes.•CNN and YOLO algorithms are proven to be effective in detecting the nodes and edges.
Deep learning and network science are applied in a synergistic manner to address structural crack issues with the aim of providing the characteristic features of crack generation and a quantitative description of crack networks in natural materials. Loess/water mixtures were chosen as a model system due to the facile formation of cracks resulting from water evaporation. Deep learning algorithms are applied to the detection and classification of edges and nodes in cracks forming in the drying stage of the loess/water mixture system. Deep learning is shown to effectively detect and classify cracks in terms of nodes and edges. Based on the guided information on nodes and edges, cracks were subject to a connectivity analysis with network science.The combined deep learning/network science approach is proven to be suitable for understanding crack formation and propagation in both qualitative and quantitative aspects. |
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| AbstractList | [Display omitted]
•Deep learning and network science are synergistically applied to the crack networks.•Network science is employed to quantify the interconnected features between nodes.•CNN and YOLO algorithms are proven to be effective in detecting the nodes and edges.
Deep learning and network science are applied in a synergistic manner to address structural crack issues with the aim of providing the characteristic features of crack generation and a quantitative description of crack networks in natural materials. Loess/water mixtures were chosen as a model system due to the facile formation of cracks resulting from water evaporation. Deep learning algorithms are applied to the detection and classification of edges and nodes in cracks forming in the drying stage of the loess/water mixture system. Deep learning is shown to effectively detect and classify cracks in terms of nodes and edges. Based on the guided information on nodes and edges, cracks were subject to a connectivity analysis with network science.The combined deep learning/network science approach is proven to be suitable for understanding crack formation and propagation in both qualitative and quantitative aspects. |
| Author | Oh, Jiwon Yoon, Young Cha, Jung-Hwan Hwang, Heesu Choi, Eunsoo Lee, Keon-Hee Hwang, Jin-Ha |
| Author_xml | – sequence: 1 givenname: Heesu surname: Hwang fullname: Hwang, Heesu organization: Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea – sequence: 2 givenname: Jiwon surname: Oh fullname: Oh, Jiwon organization: Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea – sequence: 3 givenname: Keon-Hee surname: Lee fullname: Lee, Keon-Hee organization: Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea – sequence: 4 givenname: Jung-Hwan surname: Cha fullname: Cha, Jung-Hwan organization: Department of Civil Engineering, Hongik University, Seoul 04066, South Korea – sequence: 5 givenname: Eunsoo surname: Choi fullname: Choi, Eunsoo organization: Department of Computer Engineering, Hongik University, Seoul 04066, South Korea – sequence: 6 givenname: Young surname: Yoon fullname: Yoon, Young email: young.yoon@hongik.ac.kr organization: Department of Civil Engineering, Hongik University, Seoul 04066, South Korea – sequence: 7 givenname: Jin-Ha surname: Hwang fullname: Hwang, Jin-Ha email: jhwang@hongik.ac.kr organization: Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea |
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| Keywords | Deep learning Crack detection Convolutional neural networks Complex networks |
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| Title | Synergistic approach to quantifying information on a crack-based network in loess/water material composites using deep learning and network science |
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