Machine Learning in Civil Engineering and Infrastructure Development
A Practitioner’s Handbook
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- Description
- Contents
- About
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.
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
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.