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Titlebook: Artificial Intelligence in Vision-Based Structural Health Monitoring; Khalid M. Mosalam,Yuqing Gao Book 2024 The Editor(s) (if applicable)

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21#
發(fā)表于 2025-3-25 06:36:06 | 只看該作者
Artificial Intelligence in Vision-Based Structural Health Monitoring978-3-031-52407-3Series ISSN 2573-3168 Series E-ISSN 2573-3176
22#
發(fā)表于 2025-3-25 11:27:39 | 只看該作者
The Great Prize of Mathematical Sciences,ion detection. Soukup and Huber-M?rk [.] applied CNN?to detect steel surface defects of the railway, which is a binary classification problem. Cha et al. [.] used a deep CNN?to detect concrete cracks as a binary classification without calculating the defect features.
23#
發(fā)表于 2025-3-25 13:51:45 | 只看該作者
24#
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發(fā)表于 2025-3-25 21:17:01 | 只看該作者
Drake C. Mitchell,Burton J. Litmana. If the SSL method is employed, both labeled and unlabeled data can be utilized simultaneously, which can improve the AI model performance to some extent, as demonstrated with the BSS-GAN in Chap.?..
26#
發(fā)表于 2025-3-26 03:37:01 | 只看該作者
Khalid M. Mosalam,Yuqing GaoComprehensive review of the rapidly expanding field of vision-based SHM using artificial intelligence approaches.Includes comprehensive details about the procedure of conducting AI approaches.With exa
27#
發(fā)表于 2025-3-26 07:07:24 | 只看該作者
28#
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29#
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30#
發(fā)表于 2025-3-26 18:27:02 | 只看該作者
Structural Damage Localizationbased SHM. As introduced in Chap.?., damage locations are represented by bounding boxes?with coordinates and localization algorithms and models aim to conduct a regression task to accurately predict the coordinates, which closely match the ground truth.
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