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Condizione: New. pages cm First edition Includes bibliographical references and index.
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Aggiungi al carrelloPaperback. Condizione: Brand New. 342 pages. 5.98x0.81x9.02 inches. In Stock.
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Aggiungi al carrelloCondizione: New. Mohammad Noori is a professor of mechanical engineering at California Polytechnic State University, San Luis Obispo a fellow and life member of the American Society of Mechanical Engineering (ASME) and a recipient of the Japan Society .
Lingua: Inglese
Editore: Taylor & Francis Ltd Mai 2026, 2026
ISBN 10: 1032308389 ISBN 13: 9781032308388
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.
Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2026
ISBN 10: 1032308389 ISBN 13: 9781032308388
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condizione: new. Paperback. Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers. Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides an overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2026
ISBN 10: 1032308389 ISBN 13: 9781032308388
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers. Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides an overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2026
ISBN 10: 1032308389 ISBN 13: 9781032308388
Da: AussieBookSeller, Truganina, VIC, Australia
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers. Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides an overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.