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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facilitate

Hugo W. Moser,Gerald V. RaymondStructural damage segmentation is another key task in vision-based SHM. As introduced in Chap.?2, images are labeled pixel by pixel and segmentation algorithms and models aim to recognize all pixels, group a region of pixels with the same label, and assign a class label for each region to match the ground truth.

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Commonwealth

柔美流暢

Progress in Inflammation ResearchIn previous chapters, promising results have been achieved using AI, e.g., DL-based models, in vision-based SHM problems. However, the internal working principle in AI, especially for the DL model, is hard to understand by a human and is treated as a discouraging “black box”, especially for the inquisitive engineers.

愛哭

Structural Vision Data Collection and DatasetImages and videos are the two most commonly used data types in vision-based SHM. The image represents the instantaneous state in the structure and the video provides continuous changes of the state of the structure, which is composed of a sequence of frames.

modifier

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領(lǐng)先

Semi-Supervised LearningPrevious chapters demonstrate the effectiveness of ML and DL under the supervised learning setting, where all training data are well-labeled, refer to Sect.?3.1.

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