Computer Vision and AI in Structural Health Monitoring and Structural Engineering. Woodhead Publishing Series in Civil and Structural Engineering

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.

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

Part I: Fundamentals
1. Introduction
2. Basic Concepts

Part II: Computer Vision in SHM
3. Computer Vision Fundamentals
4. CV Applications in Construction

Part III: Vision-Language Models
5. Foundation of Vision-Language Models
6. VLM Applications

Part IV: Implementation and Evaluation
7. Evaluation Metrics
8. Data Collection and Management

Part V: Advanced Topics
9. Automation Systems
10. AI and Machine Learning

Part VI: Practical Considerations
11. Implementation Guidelines
12. Case Studies

Part VII: Future Directions
13. Emerging Technologies
14. Research Opportunities

Authors

Cheng Liu City University of Hong Kong, Hong Kong. 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 City University of Hong Kong, Hong Kong. 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 City University of Hong Kong, Hong Kong. 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 City University of Hong Kong, Hong Kong. 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

Table of Contents

Authors

Related Topics

Related Products

Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures. Woodhead Publishing Series in Composites Science and Engineering

Latent Heat Storage for Energy-Efficient Buildings and Zero-Carbon Communities. Woodhead Publishing Series in Civil and Structural Engineering

Decentralized Optimization in Networks. Algorithmic Efficiency and Privacy Preservation

Digital Technology in Public Health and Rehabilitation Care. COVID Era

Applied Graph Data Science. Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases