Intelligent Predictive 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.Dr Danial Jahed Armaghani is an internationally recognised researcher and one of the most highly cited scientists globally in tunnelling, geomechanics, and AI-driven predictive modelling. He has authored ~400 peer-reviewed publications, more than 83% in Q1 journals, and has an h-index of 93 (Scopus) / 104 (Google Scholar), with more than 29,000 citations in Google Scholar. He has been consistently ranked among the top 2% of researchers worldwide (Stanford University Global Citation Ranking) from 2020 to 2025. He is also ranked among the top 0.05% of all scholars worldwide, according to ScholarGPS Highly Ranked Scholars in Engineering and Computer Science. His research has advanced theory-guided machine learning and real-time TBM performance forecasting, establishing him as a leading expert driving innovation in mechanised tunnelling 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 Research Consultant at Abu Dhabi University, she has devised machine-learning approaches that advance predictive modelling of structural response, guide optimisation of low-carbon construction materials, and inform rigorous assessments of infrastructure safety. Her scholarship appears in leading peer-reviewed outlets and has been funded by both international and national grants. Her professional service includes editorial appointments at several international journals, participation on technical committees for more than twenty international conferences, and completion of in excess of 950 peer reviews for over 80 Scopus-indexed journals. Principal research contributions comprise data-driven models for seismic vulnerability assessment and algorithms for evaluating structural resilience to seismic sequences. Her work on sustainable materials includes predictive systems for recycled-aggregate concrete, carbon-nanotube-modified cementitious composites, and FRP-strengthened elements, and she has proposed revised damage-state definitions for RC buildings that address ambiguities in seismic codes and support retrofitting strategies. Beyond peer-reviewed publications, she has produced applied resources, most notably the book Soft Computing in Civil Engineering and professional training series (neuro-fuzzy methods and optimisation) available on online learning platforms.

• Machine Learning for Sustainable Concrete Predictive Approaches Using Industrial Waste Materials.- Interpretable Machine Learning for Public Bus Service Efficiency A SHAP Driven Framework for Operational Analytics.- Smart Modeling Approaches for Foundation Settlement Forecasting A Comprehensive Review 2015 to 2025.- Computational Intelligence Approaches to Ground Settlement Prediction in Tunneling A Review of Recent Advances.- A Machine Learning Approach to California Bearing Ratio Prediction Evaluation of GEP and Ridge Regression.