Synergistic approach to quantifying information on a crack-based network in loess/water material composites using deep learning and network science
| Autor: | Jin-Ha Hwang, Heesu Hwang, Keon-Hee Lee, Eunsoo Choi, Jiwon Oh, Jung-Hwan Cha, Young Yoon |
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| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Natural materials Computer science business.industry Deep learning General Physics and Astronomy Mechanical engineering Network science Model system 02 engineering and technology General Chemistry 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Computational Mathematics Mechanics of Materials Loess General Materials Science ComputingMethodologies_GENERAL Stage (hydrology) Artificial intelligence 0210 nano-technology business |
| Zdroj: | Computational Materials Science. 166:240-250 |
| ISSN: | 0927-0256 |
| DOI: | 10.1016/j.commatsci.2019.04.014 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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