Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

Mr. Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

PhD Student, Islamic Azad University, Iran

Shayan Ghazimoghadam is a Ph.D. student in Structural Engineering at Islamic Azad University of Shahrood, Iran, specializing in data-driven structural health monitoring. His research integrates artificial intelligence with civil engineering to develop unsupervised deep learning methods for real-time damage detection in structures. Shayan’s work focuses on creating digital twins for infrastructure assessment, aiming to enhance predictive maintenance and safety. He has authored several publications, including a notable paper on vibration-based damage diagnosis using multi-head self-attention LSTM autoencoders, published in Measurement journal. Additionally, he has presented his research at national conferences and served as a keynote speaker, demonstrating his commitment to advancing the field of structural health monitoring.

Profile

Google Scholar​

Education

Shayan Ghazimoghadam completed his Bachelor of Science in Civil Engineering at Islamic Azad University of Gorgan, Iran, in 2018, where he excelled in design courses, achieving a GPA of 19/20 in Steel Structures and a perfect 20/20 in Concrete Structures. He then pursued a Master of Science in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, graduating in 2022 with a GPA of 19.07/20. His master’s dissertation focused on structural damage identification under ambient vibration using an unsupervised deep learning method, supervised by Dr. Seyed Ali Asghar Hosseinzadeh. Currently, Shayan is a Ph.D. student at Islamic Azad University of Shahrood, Iran, where he continues to explore innovative approaches in structural health monitoring and artificial intelligence applications in civil engineering.

Experience 

Between 2022 and 2023, Shayan Ghazimoghadam served as a Research Assistant at Golestan University, Gorgan, Iran, under the guidance of Dr. Seyed Ali Asghar Hosseinzadeh. During this period, he conducted research on real-time structural health monitoring utilizing AI-powered techniques. His work involved developing and testing unsupervised deep learning algorithms for damage detection in structures based on vibration data. Shayan’s contributions led to the presentation of his findings at national conferences, showcasing his ability to communicate complex research outcomes effectively. This experience has significantly enhanced his expertise in integrating artificial intelligence with structural engineering, positioning him as a promising researcher in the field of structural health monitoring.

Awards and Honors 

Shayan Ghazimoghadam has been recognized for his academic excellence and research contributions. He ranked first among M.Sc. students in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, Iran, in 2022, achieving a GPA of 19.07/20. His innovative research on structural damage identification using unsupervised deep learning methods has been published in reputable journals, including the Measurement journal, where his paper has garnered 26 citations as of 2024. Additionally, Shayan was invited as a keynote speaker at the 3rd National Conference on Civil Engineering, Intelligent Development, and Sustainable Systems in 2023, where he presented on AI-powered structural damage identification and localization through accelerometer data. These accolades underscore his commitment to advancing the field of structural health monitoring and his potential for future contributions to civil engineering research.

Research Focus

Shayan Ghazimoghadam’s research focuses on the integration of artificial intelligence with structural health monitoring (SHM) to develop innovative solutions for infrastructure maintenance. His primary interests include data-driven SHM, unsupervised structural damage identification, and the application of digital twins for condition assessment. Shayan aims to enhance the accuracy and efficiency of damage detection in structures by employing unsupervised deep learning techniques, particularly multi-head self-attention LSTM autoencoders. His work contributes to the development of digital twins, virtual representations of physical assets, to monitor and assess the condition of infrastructure in real time. By leveraging AI and machine learning, Shayan seeks to revolutionize traditional SHM practices, offering more proactive and predictive maintenance strategies that can lead to safer and more sustainable infrastructure systems.

Publication Top Notes​

📘 1. A Novel Unsupervised Deep Learning Approach for Vibration-Based Damage Diagnosis Using a Multi-Head Self-Attention LSTM Autoencoder

Authors: S. Ghazimoghadam, S.A.A. Hosseinzadeh
Journal: Measurement, Volume 229, Article 114410
Year: 2024
Citations (as of 2025): 26
DOI: Measurement 229, 114410 (sample link, please verify)

🔍 Summary:

This publication introduces a novel unsupervised deep learning method for real-time structural damage detection using only ambient vibration data. The approach combines Long Short-Term Memory (LSTM) autoencoders with multi-head self-attention mechanisms, enabling the system to effectively learn temporal features and focus on critical data patterns without the need for labeled damage data.

By leveraging unsupervised learning, the model is highly adaptable and scalable, making it suitable for practical deployment in real-world structural health monitoring (SHM) scenarios. The method was validated using benchmark datasets, showing superior performance in damage localization and diagnosis accuracy compared to traditional approaches.

📗 2. Transformer-Based Time-Series GAN for Data Augmentation in Bridge Monitoring Digital Twins

Authors: V. Mousavi, M. Rashidi, S. Ghazimoghadam, M. Mohammadi, B. Samali
Journal: Automation in Construction, Volume 175, Article 106208
Year: 2025 (Under Review)
DOI: Automation in Construction 175, 106208 (check for final link when published)

🔍 Summary:

This paper presents a Transformer-based Generative Adversarial Network (GAN) for augmenting time-series sensor data in bridge monitoring systems. The technique is particularly geared towards Digital Twin models, which require large, diverse, and high-quality datasets to simulate and predict structural behavior accurately.

The GAN architecture uses a Transformer encoder to better capture temporal dependencies in structural response data, generating realistic synthetic datasets for training SHM models. By augmenting scarce or incomplete datasets, this method improves predictive performance, anomaly detection, and damage assessment capabilities of digital twins used in civil infrastructure.

Alia Al-Ghosoun | Engineering and Technology | Best Researcher Award

Dr Alia Al-Ghosoun | Engineering and Technology | Best Researcher Award

Assistant professor, Philadephia University, Jordan

Dr. Alia Radwan Al-Ghosoun is an Assistant Professor in the Mechatronics Engineering Department at Philadelphia University, Jordan. With a deep passion for advanced engineering research, she holds a DPhil in Engineering from Durham University, UK, where her work focused on shallow water dynamics and adaptive control methods for hydrodynamic systems. Dr. Al-Ghosoun’s research spans fluid mechanics, computational modeling, and the application of artificial intelligence in engineering problems. She has worked as a post-doctoral researcher at Durham University and has held multiple academic positions at the University of Jordan, where she contributed to the development of energy-efficient systems and intelligent control techniques. Dr. Al-Ghosoun’s commitment to advancing knowledge in hydrodynamics and environmental modeling has resulted in impactful publications and contributions to numerical simulation and uncertainty quantification. She is passionate about improving the practical application of engineering solutions for environmental challenges.

Profile

Scopus

Strengths for the Award

  1. Advanced Academic Background:
    • Dr. Al-Ghosoun holds a Doctor of Philosophy in Engineering from Durham University, UK, where her research focused on shallow water flow dynamics and adaptive control techniques to improve the accuracy of these systems. This is a highly specialized field with significant implications in environmental modeling, water systems, and engineering, marking her as an expert in computational engineering and fluid dynamics.
    • Her post-doctoral research at Durham University further solidifies her expertise, particularly in understanding and quantifying uncertainty in numerical modeling of hydrodynamics, which is crucial for predicting real-world environmental phenomena.
  2. Impactful and Diverse Research Contributions:
    • Dr. Al-Ghosoun has published several peer-reviewed papers in high-impact journals such as Environmental Modelling and Software, Communications in Computational Physics, and International Journal of Computational Methods. These works cover areas such as uncertainty quantification, morphodynamics, and numerical simulation of shallow water flows and hydrosediment processes.
    • Her conference papers and book chapters demonstrate a commitment to advancing computational methods in hydrodynamics and environmental modeling, particularly addressing the challenges of bed topography deformation, fluid-structure interactions, and stress analysis in hydro-sediment systems.
  3. Interdisciplinary Research:
    • Dr. Al-Ghosoun’s research stands at the intersection of mechatronics, engineering, and environmental sciences, with a focus on adaptive control techniques and artificial intelligence. This interdisciplinary approach is essential in addressing complex real-world problems related to fluid dynamics and energy systems.
    • The integration of AI techniques (such as genetic algorithms) in energy consumption optimization and shallow water flow models highlights her innovative approach to solving large-scale engineering problems.
  4. Global Collaboration and Recognition:
    • With international experience as a Post-Doctoral Researcher at Durham University and several collaborative research efforts with Jordanian and UK-based academic institutions, Dr. Al-Ghosoun has developed a robust international network. Her involvement in global research platforms, such as ResearchGate, attests to her active engagement in the academic community and dissemination of her work.
  5. Teaching and Mentoring Experience:
    • Dr. Al-Ghosoun has demonstrated a strong commitment to education as an Assistant Professor at Philadelphia University, where she contributes to the development of young engineers in Mechatronics Engineering. Her role as a Teaching Assistant and Research Assistant at various institutions indicates her foundational experience in nurturing future engineers and scientists.
  6. Recognition of Research Excellence:
    • Dr. Al-Ghosoun’s papers, particularly her works on uncertainty quantification and modeling techniques for shallow water systems, have gained traction in the academic community. For instance, her work published in Environmental Modelling and Software (2021) has already accumulated 10 citations, signaling its importance in the field.

Areas for Improvement

  1. Broader Citation Impact:
    • While Dr. Al-Ghosoun’s work is highly specialized and impactful, the citation counts for some of her research papers remain low (e.g., her paper on stress analysis has 0 citations). Increasing visibility in wider journals and collaborating with researchers in complementary fields could enhance the reach and impact of her publications.
  2. Increased Public Engagement:
    • Engaging in public outreach or community-based projects that demonstrate the application of her research (e.g., how adaptive control methods improve water management or energy efficiency in real-world scenarios) could enhance the broader social impact of her work.
  3. Further Collaborative Interdisciplinary Projects:
    • Although her work spans several fields, further involvement in cross-disciplinary projects—especially those integrating sustainable engineering and climate resilience—could increase the relevance of her research to pressing global challenges, like climate change adaptation and sustainable resource management.

Education

Dr. Alia Radwan Al-Ghosoun earned her Doctor of Philosophy (DPhil) in Engineering from Durham University, UK in January 2021. Her doctoral research focused on understanding the effects of bathymetric movement on shallow water flows and their interaction with the seabed, leading to the development of adaptive control methods for improved accuracy in hydrodynamic simulations. Prior to this, she completed a Post-Doctorate at Durham University in 2022, where she explored the application of uncertainty quantification in complex engineering models. Dr. Al-Ghosoun holds a Master’s Degree in Mechanical Engineering from the University of Jordan, where she developed AI-based predictive models for fuel consumption in Jordan and optimized energy efficiency through genetic algorithms. She also earned her Bachelor’s degree in Mechatronics Engineering from the University of Jordan. Dr. Al-Ghosoun’s academic background equips her with interdisciplinary expertise in engineering and environmental science.

Experience

Dr. Alia Radwan Al-Ghosoun is currently an Assistant Professor at Philadelphia University in the Mechatronics Engineering Department since October 2022, where she teaches and conducts research in engineering systems and adaptive control techniques. Prior to this, she was a Post-Doctoral Researcher at Durham University, UK (2021-2022), focusing on uncertainty quantification in shallow water systems. Dr. Al-Ghosoun completed her DPhil at Durham University (2016-2021), where her research involved modeling shallow water flows and the interaction of bed topography. She has also held roles as a Research Assistant at the University of Jordan’s Water, Energy, and Environment Center (2012-2016) and the King Abdullah Design and Development Bureau (KADDB) (2012). Earlier in her career, she worked as a Teaching Assistant in both Mechatronics and Mechanical Engineering departments at the University of Jordan. Dr. Al-Ghosoun’s interdisciplinary experience blends academia with applied engineering solutions.

Awards and Honors

Dr. Alia Radwan Al-Ghosoun has been recognized for her research excellence and commitment to advancing knowledge in hydrodynamics and adaptive control systems. Her academic achievements are highlighted by her work at Durham University, where she earned a prestigious Doctoral Fellowship for her research on shallow water dynamics and bed interaction. She has also received recognition for her post-doctoral research contributions in uncertainty quantification and numerical simulations. Dr. Al-Ghosoun’s work has been presented at major academic conferences, and she has contributed to a variety of high-impact journal publications. In addition to her research accomplishments, she has been awarded teaching grants to support her role as an educator at Philadelphia University, where she mentors the next generation of Mechatronics engineers. Her consistent efforts to bridge the gap between theoretical research and practical engineering applications have earned her widespread recognition within her academic and professional communities.

Research Focus

Dr. Alia Radwan Al-Ghosoun specializes in hydrodynamic modeling, shallow water flows, and the application of adaptive control systems to improve the accuracy of complex environmental simulations. Her research interests focus on uncertainty quantification and the development of computational models for the numerical simulation of fluid dynamics, particularly in the context of stochastic bed topography and morphodynamics. She has worked extensively on shallow water waves, bathymetric effects, and water-bed interaction. One of her core research goals is to enhance the predictive accuracy of models used for environmental management and engineering systems by incorporating artificial intelligence techniques, such as genetic algorithms and surrogate models. Dr. Al-Ghosoun is passionate about integrating AI-based solutions into environmental and energy systems to address challenges like resource optimization, pollution reduction, and sustainable energy. Her work in hydro-sediment-morphodynamics provides valuable insights into climate change adaptation and water resource management.

Publication Top Notes

  1. Uncertainty quantification for stochastic morphodynamics 🌊🧑‍🔬, AIP Conference Proceedings, 2024.
  2. A Novel Computational Approach for Wind-Driven Flows over Deformable Topography 💨🌍, Lecture Notes in Computer Science, 2024.
  3. A Nonintrusive Reduced-Order Model for Uncertainty Quantification in Numerical Solution of One-Dimensional Free-Surface Water Flows Over Stochastic Beds 📊💧, International Journal of Computational Methods, 2022.
  4. Efficient Computational Algorithm for Stress Analysis in Hydro-Sediment-Morphodynamic Models 💻⚙️, Lecture Notes in Computer Science, 2022.
  5. A surrogate model for efficient quantification of uncertainties in multilayer shallow water flows 🌊🔬, Environmental Modelling and Software, 2021.
  6. A computational model for simulation of shallow water waves by elastic deformations in the topography 🌊⚡, Communications in Computational Physics, 2021.
  7. Uncertainty Quantification of Bathymetric Effects in a Two-Layer Shallow Water Model: Case of the Gibraltar Strait 🏝️🌊, Springer Water, 2020.
  8. A hybrid finite volume/finite element method for shallow water waves by static deformation on seabeds 🌊🧮, Engineering Computations, 2020.
  9. A new numerical treatment of moving wet/dry fronts in dam-break flows 💧🚨, Journal of Applied Mathematics and Computing, 2019.

Conclusion

Dr. Alia Radwan Al-Ghosoun is an exceptional candidate for the Best Researcher Award. Her contributions to the fields of hydrodynamics, uncertainty quantification, and adaptive control systems are not only advancing the understanding of complex environmental processes but are also pioneering new computational techniques that can improve the accuracy and efficiency of engineering systems. Her ability to merge artificial intelligence with environmental modeling positions her as a leader in the field. Her ongoing efforts in teaching, mentoring, and global academic collaborations further highlight her potential to shape the future of engineering and environmental sciences. With a few strategic steps to broaden her citation impact and public visibility, Dr. Al-Ghosoun could solidify her place as a thought leader in her field.

Dalia El- Gazzar | Vibration and Dynamics | Best Researcher Award

Dr Dalia El- Gazzar | Vibration and Dynamics | Best Researcher Award

Dr Dalia El- Gazzar, National water research center, Egypt

Dr. Dalia Mohamed Sadek El-Gazzar is an accomplished expert in mechanical and electrical engineering with over 24 years of experience at the Mechanical & Electrical Research Institute (MERI). Specializing in optimizing hydro-electro-mechanical systems, her work has significantly advanced predictive maintenance and dynamic analysis of pumping stations. She holds a Ph.D. in Mechanical Engineering from Menoufia University and has contributed extensively to technical research and education, including teaching advanced courses on vibration analysis and predictive maintenance. Her dedication to improving the performance and reliability of drainage and irrigation systems underscores her commitment to engineering excellence.

Publication Profile

Scopus

Strengths for the Award

  1. Extensive Experience: Dalia Mohamed Sadek El-Gazzar has over 24 years of experience at the Mechanical & Electrical Research Institute (MERI), focusing on optimizing the operation and performance of hydro-electro-mechanical components in drainage and irrigation systems. This long-standing experience is a strong point for the award.
  2. Leadership Roles: She has held significant leadership roles, such as Director Deputy and Head of the Mechanical Department at MERI. Additionally, she has led multiple research projects related to dynamic analysis and quality control in irrigation and drainage systems.
  3. Research Contributions: Dalia has published numerous papers in reputable journals, highlighting her contributions to improving the dynamic performance and reliability of pumping systems. Her work in vibration analysis and preventive maintenance is particularly noteworthy.
  4. Educational Background: With a Ph.D. in Mechanical Engineering focused on vibration analysis of pumping systems, coupled with an M.Sc. and B.Sc. in related fields, her strong academic background supports her candidacy.
  5. Technical Expertise: Dalia has technical expertise in areas such as structural and mechanical vibration, fault detection, dynamic and hydraulic assessment, and preventive maintenance of rotating machinery.
  6. Conferences and Workshops: Her participation in a wide range of international conferences and workshops demonstrates her active involvement in the research community and her commitment to continuous learning and dissemination of knowledge.

Areas for Improvement

  1. Broader Impact: While her work is highly specialized in the field of mechanical and electrical systems for water resources, expanding her research to broader applications or interdisciplinary studies might enhance her impact and visibility within the research community.
  2. International Collaboration: Although she has participated in international conferences, increasing collaboration with international researchers or institutions could strengthen her research portfolio and provide diverse perspectives.
  3. Innovation and Patents: Emphasizing innovation through the development of new technologies or securing patents could further distinguish her work and contribute to practical advancements in her field.

Education

Dr. Dalia Mohamed Sadek El-Gazzar earned her Ph.D. in Mechanical Engineering from Menoufia University in February 2012, with a focus on vibration analysis of pumping systems with variable speed drives. She completed her M.Sc. in April 2004, studying the impact of bearing faults on dynamic behavior and power consumption in water pumps. Her B.Sc., obtained in May 1999, was in Production Engineering and Mechanical Design from the same institution. Her academic background has laid a strong foundation for her expertise in vibration analysis and predictive maintenance.

Experience

Dr. El-Gazzar’s professional journey spans over two decades, with roles including Director Deputy and Head of the Mechanical Department at MERI. She has led critical research projects on dynamic analysis and quality control in irrigation and drainage systems. Her experience includes hands-on inspection, calibration, and dynamic assessment of pumping stations. She has also contributed to numerous technical investigations and reports, enhancing system performance and reliability. Her role as an educator has involved teaching advanced engineering courses and training international engineers.

Research Focus

Dr. El-Gazzar’s research focuses on the dynamic performance and reliability of hydro-electro-mechanical systems in irrigation and drainage. Her work extensively covers vibration analysis, predictive maintenance, and fault diagnosis of pumping stations. She has explored the effects of variable speed drives, bearing faults, and structural vibrations on system efficiency. Her studies aim to optimize system performance, enhance reliability, and contribute to sustainable water resource management. Her research has significantly advanced the understanding and application of dynamic analysis in improving engineering practices.

Publication Top Notes

“Enhancing Efficiency and Dynamic Performance of Bearings in Pumping Stations” 📈

“Dynamic Performance Application of A Variable Speed Centrifugal Pump” 🚀

“Effect of Critical Speed on the Dynamic and Hydraulic Performance of a Variable Speed Pump” 🔧

“Vibration Analysis of Centrifugal Pump with Variable Speed Drives” ⚙️

“Evaluating Efficiency and Safety of Aerators in a Sanitary Drainage Station Using Vibration Analysis” 🔍

“Investigate the Effect of Fan Configuration on the Performance of Aeration Units for Waste Water Treatment” 💧

“Effect of Motor Vibration Problem on the Power Quality of Water Pumping Stations” ⚡

Conclusion

Dalia Mohamed Sadek El-Gazzar is a highly qualified candidate for the Best Researcher Award, given her extensive experience, leadership roles, and significant contributions to research in the field of mechanical and electrical systems for water resources. Her work has made valuable improvements in the performance and reliability of irrigation and drainage systems. While there is room for expanding her research’s impact and international collaboration, her current achievements make her a strong contender for the award.

 

Dawit Alemayehu | Biomechanical Engineering Award | Best Researcher Award

Mr Dawit Alemayehu | Biomechanical Engineering Award | Best Researcher Award

Mr Dawit Alemayehu, Hokkaido university , Japan

Dawit Bogale Alemayehu is a dedicated researcher pursuing his PhD in Biomechanical Design at Hokkaido University, Japan, expected to graduate in September 2024. With an MSc from Addis Ababa University and a BSc from Jimma University, Ethiopia, his research focuses on advanced engineering applications like biomimetic bone structures and energy absorption materials. Dawit has published extensively in international journals and presented his work at prestigious conferences worldwide. His expertise includes CAD modeling, finite element analysis, and experimental validation. Passionate about innovation, Dawit aims to integrate cutting-edge technologies for impactful solutions in biomechanics and materials science.

Publication Profile

Orcid

Education

Dawit Bogale Alemayehu’s academic journey spans across continents and disciplines. He pursued his BSc in Mechanical Engineering at Jimma University, Ethiopia, where he focused on designing thermal systems. His MSc at Addis Ababa University delved into mechanical design, specializing in low carbon steel dynamics. Currently, Dawit is on track to complete his PhD at Hokkaido University, Japan, in Aerospace and Mechanical Engineering. His doctoral research explores cutting-edge biomechanical engineering, aiming to enhance titanium alloys and biomimetic structures for bone and energy absorption applications. Dawit’s academic path reflects a dedication to advancing engineering solutions with global impact.

Professional Experience

Dawit Bogale Alemayehu has accumulated a diverse range of experiences in academia and research. He began his career as a Graduate Assistant and Assistant Lecturer at Bahir Dar University, Ethiopia, where he taught and supported laboratory classes in Mechanical Engineering. Dawit later transitioned to roles as a Lecturer, instructing courses such as Machine Drawing and Strength of Materials. His international experience includes positions as a Research Assistant at National Taiwan University of Science and Technology and National Cheng Kung University in Taiwan, where he contributed to CAD modeling, finite element analysis, and manuscript preparation. Currently, as a PhD Fellow at Hokkaido University, Japan, Dawit conducts cutting-edge research in biomechanical engineering, aiming to publish impactful findings in international journals and present at prestigious conferences.

Research Focus

Dawit Bogale Alemayehu’s research spans several prominent areas in engineering and materials science, focusing extensively on biomechanical and biomimetic engineering. His work explores innovative applications of advanced manufacturing techniques like Fused Filament Fabrication (FFF) to create bioinspired lattice structures for enhanced energy absorption. Additionally, he conducts Finite Element Analysis (FEA) studies to optimize dental implants with biomimetic trabecular bone designs. Alemayehu’s research also delves into improving the biological and mechanical properties of materials such as pure titanium through processes like Equal Channel Angular Pressing (ECAP) and Micro-Arc Oxidation. His contributions emphasize the intersection of engineering innovation and biomedical applications, aiming to advance both theoretical understanding and practical applications in these fields. 🌟

Publication Top Notes