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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.
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.
