This book presents smart optimization techniques and adaptive intelligent control systems which stimulate innovations in engineering and computational science. This book captures these innovations and describes multiple applications of various advanced algorithms. The book encompasses a diverse range of applications and illustrates the value of these computational techniques; notable applications include the attitude control of CubeSats, the performance of building components during seismic events, the efficiency of tunnel-boring machines, and forecasting of environmental and structural phenomena. A major theme presented within the book is the development of advanced intelligent systems, including self-optimizing modules, as reflected in research advances on Digital Twins and Omniverse technologies, where real-time supervision and control are achieved through virtual replicas of physical entities and spatial environments. The incorporation of artificial intelligence (AI) provides essential simplifications of otherwise opaque models, yielding interpretable insights that facilitate process automation. By connecting rigorous theoretical work with real-world problems, the book serves as a guide for researchers, engineers, and students, which offers a comprehensive picture of advanced developments in the area of adaptive optimized systems, alongside the theory and practice necessary to innovate within systems engineering.
Table of Contents:
Nonlinear and Cyclical Drivers of Norway’s Fishing Load Capacity: Evidence from Fourier-Machine Learning Approaches.- Digital Twin and AI-Driven Optimization Framework for Urban Water Management Strategies in Bahrain.- Design and Comparative Analysis of Similar Proportional Derivative and Backstepping Controllers for CubeSat Attitude Control.- From Digital Twin to Omniverse: A Comparative Study of Next Generation Simulation Ecosystems.- Assessment of Tunnel Boring Machine Performance using Geological and Operational Parameters: A Fundamental Investigation of Feature Selection and Multicollinearity-Sensitivity Relationship.- Enhancing Performance of Parsimonious Spiking Neural Networks with Astrocyte in Few-Shot Image Recognition.- Response Surface Methodology for Calculating Crack Width in Reinforced Concrete Beams with GFRP Bars.- Trajectory Tracking of a Two-Axis Gimbal System Using a PSO-Optimized Sliding Mode Controller.- Enhancing Thermal Comfort Prediction Models in Hospital Buildings: A Machine Learning Approach.- Review on the Application of Optimization Algorithms for Shallow Foundation Design.- ML-based Practical Solution of Equivalent Viscous Damping in Diagonally Reinforced Coupling Beams.
About the Author :
Dr. Aydin Azizi holds a Ph.D. in mechanical engineering–mechatronics. 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 and automation, AI, and simulation techniques.
Dr. Danial Jahed Armaghani is an internationally recognized researcher and one of the most highly cited scientists globally in tunneling, geomechanics, and AI-driven predictive modeling. His research has advanced theory-guided machine learning and real-time TBM performance forecasting, establishing him as a leading expert driving innovation in mechanized tunneling and intelligent underground construction.
Dr. Mirrashid applies computational intelligence methods to problems in structural and earthquake engineering, with an emphasis on reducing the environmental footprint of built infrastructure. In her capacity as a research consultant at Abu Dhabi University, she has devised machine learning approaches that advance predictive modeling of structural response, guide optimization of low-carbon construction materials, and inform rigorous assessments of infrastructure safety.
