Computer Vision and AI in Structural Health Monitoring and Structural Engineering

Nyhet

Häftad, Engelska, 2026

Av Cheng Liu, Yingchao Zhang, Xuebing Xu, Yan Chen

2 349 kr

Kommande

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. 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.

Provides in-depth coverage of how computer vision and AI technologies transform structural health monitoring (SHM)
Focuses on the emerging vision-language models, which enable automated defect description, multimodal damage assessment, and natural language interfaces for SHM systems, making monitoring processes more intuitive and efficient for users
Features case studies on bridge monitoring systems, building inspections, and infrastructure maintenance projects, showcasing successful implementations of advanced SHM techniques
Explores cutting-edge technologies like 5G, edge computing, advanced sensors, and extended reality, highlighting their potential role in the future of SHM and offering readers a forward-looking perspective on the field

Produktinformation

Utgivningsdatum2026-05-01
Mått152 x 229 x undefined mm
FormatHäftad
SpråkEngelska
SerieWoodhead Publishing Series in Civil and Structural Engineering
Antal sidor250
FörlagElsevier Science
ISBN9780443450228

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Dr. Liu received his PhD from the Department of Mechanical Engineering at Stanford University and an M.Sc. in Aeronautics and Astronautics, also from Stanford University. Cheng Liu's research is focused on physics-guided machine learning for structural health monitoring (SHM), smart structures, cyber-physical systems/digital twin, robotic tactile sensing and the mechanics of composite structures. His recent research includes the fusion of data-driven and physics-based methods for SHM to improve its robustness and explainability, so that SHM can really be widely applied in real-world scenarios Yingchao Zhang is currently pursuing a PhD degree in Systems Engineering at the City University of Hong Kong. He received his bachelor's and master’s degrees in civil engineering from Shandong University. His main research interest is in intelligent detection of transport infrastructure Xuebing Xu is currently pursuing a PhD degree in Systems Engineering at the City University of Hong Kong. He received his bachelor's and master’s degrees from Huazhong University of Science and Technology. His main research includes the development and application of vision language models and large language models Yan Chen is currently pursuing a PhD degree in Systems Engineering at the City University of Hong Kong. He received his bachelor's from the National University of Defense Technology, China, and a masters degree from the City University of Hong Kong. His main research includes the development and application of deep learning and large language models

Part I: Fundamentals1. Introduction2. Basic ConceptsPart II: Computer Vision in SHM3. Computer Vision Fundamentals4. CV Applications in ConstructionPart III: Vision-Language Models5. Foundation of Vision-Language Models6. VLM ApplicationsPart IV: Implementation and Evaluation7. Evaluation Metrics8. Data Collection and ManagementPart V: Advanced Topics9. Automation Systems10. AI and Machine LearningPart VI: Practical Considerations11. Implementation Guidelines12. Case StudiesPart VII: Future Directions13. Emerging Technologies14. Research Opportunities