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020 _a9783031524073
_9978-3-031-52407-3
024 7 _a10.1007/978-3-031-52407-3
_2doi
050 4 _aTH3301-3411
072 7 _aTNKX
_2bicssc
072 7 _aTEC009020
_2bisacsh
072 7 _aTNKX
_2thema
100 1 _aMosalam, Khalid M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_971711
245 1 0 _aArtificial Intelligence in Vision-Based Structural Health Monitoring
_h[electronic resource] /
_cby Khalid M. Mosalam, Yuqing Gao.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXXXV, 374 p. 171 illus., 161 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Mechanical Engineering,
_x2573-3176
505 0 _a1. Introduction -- Part I Preliminaries -- 2. Vision Tasks in Structural Health Monitoring -- 3. Basics of Machine Learning -- 4. Basics of Deep Learning -- Part II: Introducing AI to Vision-based SHM -- 5. Structural Vision Data Collection & Dataset -- 6. Transfer Learning for Image Recognition -- 7. Structural Damage Detection (Localization) -- 8. Structural Damage Segmentation -- Part III: Advanced topics of AI in Vision-based SHM -- 9. Generative Adversarial Network for Structural Image Data Augmentation -- 10. Semi-Supervised Learning -- 11. Active Learning -- Part IV: Resilient AI Applications in Vision-based SHM -- 12. Multi-Modal Learning -- 13. Multi-Task Learning -- 14. Interpreting CNN in Structural Vision Tasks -- 15. Future Extensions.
520 _aThis book introduces and implements the state-of-the-art machine learning and deep learning technologies for vision-based SHM applications. Specifically, corresponding to the above-mentioned scientific questions, it consists of: (1) motivation, background & progress of AI-aided vision-based SHM, (2) fundamentals of machine learning & deep learning approaches, (3) basic AI applications in vision-based SHM, (4) advanced topics & approaches, and (5) resilient AI-aided applications. In the introduction, a brief coverage about the development progress of AI technologies in the vision-based area is presented. It gives the readers the motivations and background of the relevant research. In Part I, basic knowledges of machine and deep learning are introduced, which provide the foundation for the readers irrespective of their background. In Part II, to verify the effectiveness of the AI methods, the key procedure of the typical AI-aided SHM applications (classification, localization, and segmentation) is explored, including vision data collection, data pre-processing, transfer learning-based training mechanism, evaluation, and analysis. In Part III, advanced AI topics, e.g., generative adversarial network, semi-supervised learning, and active learning, are discussed. They aim to address several critical issues in practical projects, e.g., the lack of well-labeled data and imbalanced labels, to improve the adaptability of the AI models. In Part IV, the new concept of “resilient AI” is introduced to establish an intelligent disaster prevention system, multi-modality learning, multi-task learning, and interpretable AI technologies. These advances are aimed towards increasing the robustness and explainability of the AI-enabled SHM system, and ultimately leading to improved resiliency. The scope covered in this book is not only beneficial for education purposes but also is essential for modern industrial applications. The target audience is broad and includes students, engineers, and researchers in civil engineering, statistics, and computer science.
650 0 _aBuildings
_xRepair and reconstruction.
650 0 _aBuildings
_xMaintenance.
650 0 _aArtificial intelligence.
_958441
650 0 _aImage processing
_xDigital techniques.
_964228
650 0 _aComputer vision.
650 1 4 _aBuilding Repair and Maintenance.
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_965550
700 1 _aGao, Yuqing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_971712
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031524066
776 0 8 _iPrinted edition:
_z9783031524080
776 0 8 _iPrinted edition:
_z9783031524097
830 0 _aSynthesis Lectures on Mechanical Engineering,
_x2573-3176
_965545
856 4 0 _uhttps://doi.org/10.1007/978-3-031-52407-3
912 _aZDB-2-SXSC
999 _c57868
_d57868