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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.
M. Z. Naser PhD, PE is Assistant Professor at the School of Civil and Environmental Engineering and Earth Sciences and a member of the Artificial Intelligence Research Institute for Science and Engineering (AIRISE) Clemson University, USA.
Part I – Foundations and Modeling Philosophy (Conceptual overview, reviews and modeling mindsets)
Chapter 1. Overview of machine learning in civil engineering
Chapter 2. Physics informed machine learning: Applications in smart transportation
Chapter 3. Sustainable selection of construction materials using machine learning – A review
Part II – Structural Performance and material analysis
Chapter 4. The power of carefully specified linear models: steel fibre-reinforced concrete and its variability
Chapter 5. Machine Learning-Guided Mechanical Characterization of 3D-Printed Plastic Materials Towards Future Optimization of Additive Manufactured Infrastructure Components
Chapter 6. Deep Learning-Based Surface Crack Detection in Fiber-Reinforced Concrete Exposed to Temperature Variations
Chapter 7. Machine learning-driven Approach to understanding the Punching Shear Design in Steel Fiber-Reinforced Slabs
Chapter 8. Training of ANN using Mountain Gazelle Optimization for Fire Resistance of FRP-Strengthened Beams
Part III – Systems, Infrastructure, and Practice
Chapter 9. Saltspot: A Convolutional Neural Network Approach for Classifying Salt Contamination Damage on Civil Infrastructure
Chapter 10. Machine Learning Driven Assessment of embodied GHG emissions of Structural Systems
Chapter 11. Synergising Human Expertise and AI in Flood Forecasting, Management and Resilience
Chapter 12. A Critical Evaluation of AI Chatbot’s Suggestions for DFWS, JEDI, Machine Learning, and Modernization Strategies