The introduction of AI and ML technologies has brought new changes to the engineering profession. This book presents the theory and practical solutions backed with real results. It also explains in detail the uses of Machine Learning methods, Gene Expression Programming, Extreme Gradient Boosting, and Deep Neural Networks in predicting parameters that are critical in understanding soil behavior, foundation settlements, material behavior, resource consumption, and beyond. One focal point is the shift from opaque models to transparent, accountable AI. The integration of AI methods is elucidated to clarify decisions made by predictive models and instill trust in the predictive systems. Additionally, the book addresses the issue of sustainability by demonstrating how AI can refine the utilization of industrial by-products such as fly ash and marble slurry in the construction sector and improve the efficiency of public transportation systems.

Dr. Aydin Azizi holds a PhD in Mechanical Engineering–Mechatronics, an MSc in Mechatronics, and a BSc in Mechanical Engineering. Certified as a Fellow of the Higher Education Academy, official instructor for the Siemens Mechatronic Certification Program (SMSCP), and Editor-in-Chief of the book series Emerging Trends in Mechatronics published by Springer Nature Group, he currently serves as a Senior Lecturer and the Academic Partnership Liaison Manager at Oxford Brookes University. His current research focuses on investigating and developing novel techniques to model, control, and optimize complex systems, with expertise in Control & Automation, AI, and Simulation Techniques. Dr. Azizi is the recipient of the National Research Award of Oman for his AI-based controllers research, DELL EMC’s “Envision the Future” award for the “Automated Irrigation System,” and ‘Exceptional Talent’ recognition by the British Royal Academy of Engineering. He has also been recognized for three consecutive years (2023–2025) among the World’s Top 2% Scientists by Stanford University & Elsevier for his impactful research contributions.