05027nam a22005535i 4500 978-3-031-52407-3 DE-He213 20251030111443.0 cr nn 008mamaa 240225s2024 sz | s |||| 0|eng d 9783031524073 978-3-031-52407-3 10.1007/978-3-031-52407-3 doi TH3301-3411 TNKX bicssc TEC009020 bisacsh TNKX thema Mosalam, Khalid M. author. aut http://id.loc.gov/vocabulary/relators/aut 71711 Artificial Intelligence in Vision-Based Structural Health Monitoring [electronic resource] / by Khalid M. Mosalam, Yuqing Gao. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XXXV, 374 p. 171 illus., 161 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Synthesis Lectures on Mechanical Engineering, 2573-3176 1. 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. This 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. Buildings Repair and reconstruction. Buildings Maintenance. Artificial intelligence. 58441 Image processing Digital techniques. 64228 Computer vision. Building Repair and Maintenance. Artificial Intelligence. 58441 Computer Imaging, Vision, Pattern Recognition and Graphics. 65550 Gao, Yuqing. author. aut http://id.loc.gov/vocabulary/relators/aut 71712 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031524066 Printed edition: 9783031524080 Printed edition: 9783031524097 Synthesis Lectures on Mechanical Engineering, 2573-3176 65545 https://doi.org/10.1007/978-3-031-52407-3 ZDB-2-SXSC 57868 57868