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Continuum numerical modeling of dynamic crack propagation has been a great challenge over the past decade. This is particularly the case for anticracks in porous materials, as reported in sedimentary rocks, deep earthquakes, landslides, and snow avalanch
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According to the material library provided by FRANC3D, the parameters of the tested material, A286, are shown in Table1. Table 1 Material properties as used in the NASGRO version 3 equation (Eq.9) for A286. Full size table Fatigue crack propagation test and the validation of the simulation...
Dataset for Fracture and Impact Toughness of High-Entropy Alloys Article Open access 19 January 2023 The role of mineralogical and textural complexity in the damage evolution of brittle rocks Article Open access 19 November 2024 Superalloys fracture process inference based on overlap analysis of ...
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DeepCrack: Learning hierarchical convolutional features for crack detection. IEEE Trans. Image Process. 2019, 28, 1498–1512. [Google Scholar] [CrossRef] [PubMed] Feng, C.; Liu, M.Y.; Kao, C.C.; Lee, T.Y. Deep active learning for civil infrastructure defect detection and classification....
Accurate crack detection based on distributed deep learning for IoT environment. Sensors 2023, 23, 858. [Google Scholar] [CrossRef] Hu, K.; Chen, Z.; Kang, H.; Tang, Y. 3D vision technologies for a self-developed structural external crack damage recognition robot. Autom. Constr. 2024, ...