PhD Research, Newcastle University (2019–2024):
Developed hybrid DT models for real-time SHM by integrating physics-based models with data-driven approaches. This work enables proactive maintenance through real-time damage detection and predictive algorithms, advancing infrastructure management.
Published two conference papers, with one awarded Best Paper Award at the 8th International Conference on Civil Structural and Transportation Engineering (ICCSTE'23). Currently, one manuscript is under review at a peer-reviewed journal.
Skills
Digital Twins and SHM: Expertise in developing hybrid models combining RB Methods and machine learning to enhance SHM systems for infrastructure.
IoT Systems: Developed cost-effective IoT-based SHM hardware for real-time data collection and DT integration.
Programming: Proficient in Python, MATLAB, and SQL for machine learning, data analysis, and structural simulations.
Advanced Techniques: Knowledge of generative AI, physics-informed neural networks, damage detection algorithms, and finite element analysis.
Data Platforms: Proficient in Databricks for efficient data management and analysis.
Research Interests
Specialising in generative AI and dynamic diffusion models to enhance AI efficiency in SHM systems.
Developing self-generating digital twins that adapt in real-time to improve system performance.
Exploring the integration of self-healing materials with digital twins for enhanced structural resilience.
Education
PhD, Civil Engineering – Newcastle University (2019–Expected December 2024).
MSc, Structural Engineering – Vilnius Gediminas Technical University (2017–2019).
BSc, Civil Engineering – German University in Cairo (2012–2017).
Teaching Experience
Part-time Teaching Assistant, Newcastle University (2020–Present): Supporting courses such as Computational Engineering Analysis, Structural Analysis, and supervising practical labs.
Certifications
Certified in AutoCAD, Revit, MATLAB, IoT, Artificial Intelligence, and Digital Twin technologies, with further expertise in programming and data-driven decision-making.
Awards
Best Paper Award at ICCSTE'23 for research on Digital Twin technology in real-time structural anomaly detection.
Professional Memberships
Associate Fellow of Advance HE (AFHEA), member of the Institution of Civil Engineers (ICE), Institution of Structural Engineers (IStructE), American Society of Civil Engineers (ASCE), Structural Engineering Institute (SEI), and IEEE.
Publications
Published papers on topics including high-strength steel cold-formed sections, digital twin applications, and IoT in SHM.