9781836628675 - machine learning in civil engineering and infrastructure development: a practitioner's handbook di naser, m z (14 risultati)
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Hardcover. Condizione: new. Hardcover. Machine Learning in Civil Engineering and Infrastructure Development: A Practitioner's Handbook is a practitioner-oriented handbook that demonstrates, through diverse real-world examples, how civil engineers can integrate machine learning into projects while remaining grounded in engineerin…g judgment, physical understanding, and professional responsibility.The book is organised in three parts, guiding readers from foundational principles to advanced applications. Part I introduces core machine learning concepts and workflows to establish the modelling philosophy that underpins later chapters. Part II explores applications at the material and structural level, including damage detection, durability under extreme conditions, and optimisation of emerging technologies such as 3D printing. Part III expands to system-level challenges and professional practice by covering topics like condition assessment using computer vision, embodied carbon estimation, flood risk management through humanAI collaboration, and critical reflections on ethics, AI tools and the modernisation of the profession.Bridging the gap between complex machine learning methodologies and practical implementation, this book equips civil engineering professionals with the knowledge and skills to stay at the forefront of their industry. Educators will also find case studies for teaching, while researchers can draw inspiration for new datasets, hybrid models, and integration into codes and standards. Machine Learning in Civil Engineering and Infrastructure Development is a guide to core concepts of machine learning and applications in civil engineering. From complex machine learning methodologies to real-world applications, it gives professionals the knowledge and tools to leverage these techniques effectively in their work. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Hardcover. Condizione: new. Hardcover. Machine Learning in Civil Engineering and Infrastructure Development: A Practitioner's Handbook is a practitioner-oriented handbook that demonstrates, through diverse real-world examples, how civil engineers can integrate machine learning into projects while remaining grounded in engineerin…g judgment, physical understanding, and professional responsibility.The book is organised in three parts, guiding readers from foundational principles to advanced applications. Part I introduces core machine learning concepts and workflows to establish the modelling philosophy that underpins later chapters. Part II explores applications at the material and structural level, including damage detection, durability under extreme conditions, and optimisation of emerging technologies such as 3D printing. Part III expands to system-level challenges and professional practice by covering topics like condition assessment using computer vision, embodied carbon estimation, flood risk management through humanAI collaboration, and critical reflections on ethics, AI tools and the modernisation of the profession.Bridging the gap between complex machine learning methodologies and practical implementation, this book equips civil engineering professionals with the knowledge and skills to stay at the forefront of their industry. Educators will also find case studies for teaching, while researchers can draw inspiration for new datasets, hybrid models, and integration into codes and standards. Machine Learning in Civil Engineering and Infrastructure Development is a guide to core concepts of machine learning and applications in civil engineering. From complex machine learning methodologies to real-world applications, it gives professionals the knowledge and tools to leverage these techniques effectively in their work. 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.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Buch. Condizione: Neu. Machine Learning in Civil Engineering and Infrastructure Development | A Practitioner's Handbook | M Z Naser | Buch | Englisch | 2026 | Emerald Group Publishing Limited | EAN 9781836628675 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: p…reigu Print on Demand.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning in Civil Engineering and Infrastructure Development: A Practitioner's Handbook is a practitioner-oriented handbook that demonstrates, through diverse real-world examples, how civil engineers can integrate machine learning into… projects while remaining grounded in engineering judgment, physical understanding, and professional responsibility.The book is organised in three parts, guiding readers from foundational principles to advanced applications. Part I introduces core machine learning concepts and workflows to establish the modelling philosophy that underpins later chapters. Part II explores applications at the material and structural level, including damage detection, durability under extreme conditions, and optimisation of emerging technologies such as 3D printing. Part III expands to system-level challenges and professional practice by covering topics like condition assessment using computer vision, embodied carbon estimation, flood risk management through human-AI collaboration, and critical reflections on ethics, AI tools and the modernisation of the profession.Bridging the gap between complex machine learning methodologies and practical implementation, this book equips civil engineering professionals with the knowledge and skills to stay at the forefront of their industry. Educators will also find case studies for teaching, while researchers can draw inspiration for new datasets, hybrid models, and integration into codes and standards.
