Computer Vision and AI in Structural Health Monitoring and Structural Engineering explores cutting-edge approaches to SHM, integrating advancements in computer vision, artificial intelligence (AI), and multimodal technologies to revolutionize how infrastructure is monitored, maintained, and managed. Starting with the fundamentals of SHM and structural engineering, the book examines the transformative power of computer vision applications, such as crack detection, corrosion assessment, and real-time deformation analysis. It also introduces vision-language models (VLMs), enabling automated defect reporting, multimodal analysis, and natural language interfaces for SHM systems.
In an era of aging infrastructure and an increasing demand for safety, structural health monitoring (SHM) has become critical for ensuring the longevity and reliability of buildings, bridges, and other essential structures. This book explores these important concepts.
1. Chapter 1 Introduction - p. 3 2. Chapter 2 Basic concepts - p. 17 3. Part II: Computer vision in structural health monitoring - p. P2 4. Chapter 3 Computer vision fundamentals - p. 33 5. Chapter 4 CV applications in construction - p. 51 6. Part III: Vision-language models - p. P3 7. Chapter 5 Foundation of vision-language models - p. 69 8. Chapter 6 Vision-language model applications in structural health monitoring - p. 85 9. Part IV: Implementation and evaluation - p. P4 10. Chapter 7 Evaluation metrics - p. 99 11. Chapter 8 Data collection and management - p. 109 12. Part V: Advanced topics - p. P5 13. Chapter 9 Automation systems - p. 117 14. Chapter 10 Data fusion in structural health monitoring - p. 139 15. Part VI: Practical considerations - p. P6 16. Chapter 11 Implementation guidelines - p. 161 17. Chapter 12 Case studies - p. 169 18. Part VII: Future directions - p. P7 19. Chapter 13 Visionary horizons: Computer vision and artificial intelligence shaping the future of structural health monitoring - p. 185 20. Chapter 14 Innovative frontiers: Research opportunities - p. 199 21. Glossary - p. 209 22. Index - p. 213
Computer Vision and AI in Structural Health Monitoring and Structural Engineering explores cutting-edge approaches to SHM, integrating advancements in computer vision, artificial intelligence (AI), and multimodal technologies to revolutionize how infrastructure is monitored, maintained, and managed. Starting with the fundamentals of SHM and structural engineering, the book examines the transformative power of computer vision applications, such as crack detection, corrosion assessment, and real-time deformation analysis. It also introduces vision-language models (VLMs), enabling automated defect reporting, multimodal analysis, and natural language interfaces for SHM systems.
In an era of aging infrastructure and an increasing demand for safety, structural health monitoring (SHM) has become critical for ensuring the longevity and reliability of buildings, bridges, and other essential structures. This book explores these important concepts.
1. Chapter 1 Introduction - p. 3 2. Chapter 2 Basic concepts - p. 17 3. Part II: Computer vision in structural health monitoring - p. P2 4. Chapter 3 Computer vision fundamentals - p. 33 5. Chapter 4 CV applications in construction - p. 51 6. Part III: Vision-language models - p. P3 7. Chapter 5 Foundation of vision-language models - p. 69 8. Chapter 6 Vision-language model applications in structural health monitoring - p. 85 9. Part IV: Implementation and evaluation - p. P4 10. Chapter 7 Evaluation metrics - p. 99 11. Chapter 8 Data collection and management - p. 109 12. Part V: Advanced topics - p. P5 13. Chapter 9 Automation systems - p. 117 14. Chapter 10 Data fusion in structural health monitoring - p. 139 15. Part VI: Practical considerations - p. P6 16. Chapter 11 Implementation guidelines - p. 161 17. Chapter 12 Case studies - p. 169 18. Part VII: Future directions - p. P7 19. Chapter 13 Visionary horizons: Computer vision and artificial intelligence shaping the future of structural health monitoring - p. 185 20. Chapter 14 Innovative frontiers: Research opportunities - p. 199 21. Glossary - p. 209 22. Index - p. 213
