Computer Vision and AI in Structural Health Monitoring and Structural Engineering
Language: English
Published by Elsevier Science Mai 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
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Add to basketItem description from seller
Neuware - 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.
Seller Inventory # 9780443450228
- Title
- Computer Vision and AI in Structural Health Monitoring and Structural Engineering
- Author
- Yingchao Zhang
- Publisher
- Elsevier Science Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0443450226
- ISBN 13
- 9780443450228
- Item weight
- 544 grams
- Dimensions
- 275x219x8 mm
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.
- Provides in-depth coverage on how computer vision and AI technologies transform structural health monitoring (SHM)
- Focuses on the emerging vision-language models that 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 forward-looking perspectives on the field
"Synopsis" may belong to another edition of this title.
About the Author
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
"About the title" may belong to another edition of this title.
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