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Artificial Intelligence for Structural Health Monitoring: Smart Solutions for Safer Infrastructure - Softcover
Synopsis
Artificial Intelligence for Structural Health Monitoring: Smart Solutions for Safer Infrastructure
Modern infrastructure is aging, complex, and under increasing strain. Bridges, skyscrapers, tunnels, and transit systems form the backbone of our daily lives, yet traditional inspection methods are often costly, time-consuming, and limited in scope. This book demonstrates how Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way engineers and inspectors safeguard critical structures.
Written for civil engineers, façade inspectors, researchers, and graduate students, this comprehensive guide bridges the gap between advanced AI methods and practical field applications in Structural Health Monitoring (SHM). From vibration analysis and sensor technologies to machine learning algorithms and predictive maintenance, readers will gain the knowledge to integrate AI into real-world infrastructure management.
Key features include:
A practical introduction to SHM fundamentals and sensor technologies.
Step-by-step coverage of machine learning, deep learning, and anomaly detection methods for structural assessment.
Real-world case studies on bridges, high-rise buildings, and transportation networks.
Dedicated insights into New York City’s Façade Inspection & Safety Program (LL11/FISP), showing how AI enhances compliance and urban resilience.
Discussion of big data, model validation, ethical considerations, and future trends in intelligent infrastructure.
By combining engineering principles with cutting-edge AI, this book equips professionals and researchers to design safer, more efficient, and more sustainable monitoring systems. It is both a reference for seasoned engineers and a roadmap for students entering this rapidly evolving field.
"synopsis" may belong to another edition of this title.
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Artificial Intelligence for Structural Health Monitoring: Smart Solutions for Safer Infrastructure
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Artificial Intelligence for Structural Health Monitoring (Paperback)
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Paperback. Condition: new. Paperback. Artificial Intelligence for Structural Health Monitoring: Smart Solutions for Safer InfrastructureModern infrastructure is aging, complex, and under increasing strain. Bridges, skyscrapers, tunnels, and transit systems form the backbone of our daily lives, yet traditional inspection methods are often costly, time-consuming, and limited in scope. This book demonstrates how Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way engineers and inspectors safeguard critical structures.Written for civil engineers, facade inspectors, researchers, and graduate students, this comprehensive guide bridges the gap between advanced AI methods and practical field applications in Structural Health Monitoring (SHM). From vibration analysis and sensor technologies to machine learning algorithms and predictive maintenance, readers will gain the knowledge to integrate AI into real-world infrastructure management.Key features include: A practical introduction to SHM fundamentals and sensor technologies.Step-by-step coverage of machine learning, deep learning, and anomaly detection methods for structural assessment.Real-world case studies on bridges, high-rise buildings, and transportation networks.Dedicated insights into New York City's Facade Inspection & Safety Program (LL11/FISP), showing how AI enhances compliance and urban resilience.Discussion of big data, model validation, ethical considerations, and future trends in intelligent infrastructure.By combining engineering principles with cutting-edge AI, this book equips professionals and researchers to design safer, more efficient, and more sustainable monitoring systems. It is both a reference for seasoned engineers and a roadmap for students entering this rapidly evolving field. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798266139145
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Artificial Intelligence for Structural Health Monitoring (Paperback)
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Paperback. Condition: new. Paperback. Artificial Intelligence for Structural Health Monitoring: Smart Solutions for Safer InfrastructureModern infrastructure is aging, complex, and under increasing strain. Bridges, skyscrapers, tunnels, and transit systems form the backbone of our daily lives, yet traditional inspection methods are often costly, time-consuming, and limited in scope. This book demonstrates how Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way engineers and inspectors safeguard critical structures.Written for civil engineers, facade inspectors, researchers, and graduate students, this comprehensive guide bridges the gap between advanced AI methods and practical field applications in Structural Health Monitoring (SHM). From vibration analysis and sensor technologies to machine learning algorithms and predictive maintenance, readers will gain the knowledge to integrate AI into real-world infrastructure management.Key features include: A practical introduction to SHM fundamentals and sensor technologies.Step-by-step coverage of machine learning, deep learning, and anomaly detection methods for structural assessment.Real-world case studies on bridges, high-rise buildings, and transportation networks.Dedicated insights into New York City's Facade Inspection & Safety Program (LL11/FISP), showing how AI enhances compliance and urban resilience.Discussion of big data, model validation, ethical considerations, and future trends in intelligent infrastructure.By combining engineering principles with cutting-edge AI, this book equips professionals and researchers to design safer, more efficient, and more sustainable monitoring systems. It is both a reference for seasoned engineers and a roadmap for students entering this rapidly evolving field. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798266139145
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