Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

Published on by Popular Engineer

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.

Pedro Silva | Earthquake Engineering Award | Best Researcher Award

Published on by Popular EngineerLeave a Comment on Pedro Silva | Earthquake Engineering Award | Best Researcher Award

Dr Pedro Silva | Earthquake Engineering Award | Best Researcher Award

Dr Pedro Silva, The George Washington University, United States

Dr. Pedro Franco Silva is a Professor at The George Washington University, specializing in civil engineering with a focus on structural design and seismic performance. He began his career in architectural design firms in California, where he gained over 10 years of experience. Dr. Silva earned his BS and MS from the University of California at Irvine while working full-time. He later transitioned to structural engineering, working for over two years designing structural systems for buildings. Dr. Silva then pursued academia, earning his PhD from the University of California at San Diego. With over 20 years in academia, he has contributed significantly to research on bridge and building structures, focusing on innovative design and experimentation.

Publication Profile

Google scholar

Strengths for the Award

  1. Extensive Experience: Dr. Silva has over 20 years of academic experience and over 12 years in industry, bringing a wealth of practical and theoretical knowledge to his work.
  2. Innovative Research: His research focuses on innovative procedures for civil structures to resist both natural and man-made hazards. His work on unbonded post-tensioned shear walls, seismic design practices, and stochastic methodologies for bridge safety highlights his contributions to advancing structural engineering.
  3. Publications and Citations: With 68 journal publications and a citation index of 25, Dr. Silva has made significant contributions to his field, demonstrating the impact and recognition of his work.
  4. Patents and Editorial Role: He holds a patent for a mechanical device for prestressing CFRP sheets and serves as an Associate Editor for the journal Structures, showcasing his leadership and innovation.
  5. Collaborations and Memberships: His collaborations with notable researchers and his memberships in professional organizations like EERI, ACI, and ASEE reflect his active engagement and influence in the engineering community.
  6. Contributions to Standards and Guidelines: Dr. Silva’s leadership in developing seismic strengthening guidelines for concrete buildings using FRP composites is a notable achievement, indicating his contributions to establishing industry standards.

Areas for Improvements

  1. Books Published: Dr. Silva has not published any books. Authoring a book could further establish his expertise and reach a broader audience.
  2. Consultancy and Industry Engagement: While he has significant industry experience early in his career, recent and ongoing consultancy projects or industry collaborations could strengthen his application by demonstrating continued practical impact.
  3. Broader Citation Index: Although a citation index of 25 is commendable, further increasing this number through more high-impact publications and citations could enhance his academic standing.

Education  

Dr. Pedro Franco Silva’s academic journey is a testament to his dedication and excellence in civil engineering. He obtained his Bachelor of Science (BS) and Master of Science (MS) degrees from the University of California at Irvine, where he honed his skills while balancing full-time work in architectural design firms. His passion for structural engineering led him to pursue a PhD from the University of California at San Diego, specializing in the design and experimentation of structural systems. Dr. Silva’s advanced education has equipped him with the knowledge and expertise to innovate and lead in the field of civil engineering, particularly in the areas of seismic performance and structural resilience.

Experience 

Dr. Pedro Franco Silva’s professional experience bridges both industry and academia. He began his career with over 10 years in architectural design firms in California, where he developed a strong foundation in designing complex structures. Following this, he spent more than two years at a structural engineering firm, focusing on the design of building systems. Transitioning to academia, Dr. Silva has spent over 20 years teaching and conducting groundbreaking research at The George Washington University. His extensive experience includes developing innovative design procedures for civil structures to withstand man-made and natural hazards. Dr. Silva’s dual background in industry and academia provides him with a unique perspective, enabling him to contribute significantly to both practical applications and theoretical advancements in civil engineering.

Awards and Honors  

Dr. Pedro Franco Silva’s contributions to civil engineering have been recognized with numerous awards and honors. He is a voting member of the American Concrete Institute (ACI) Committees 440, where he played a pivotal role in developing the first worldwide code of practice for the seismic strengthening of concrete buildings using FRP composites. Dr. Silva has also been acknowledged for his research on seismic design recommendations for bridge structures, funded by the Federal Highway Administration (FHWA) and the Alaska Department of Transportation. His role as an Associate Editor for the journal “Structures” further underscores his leadership and influence in the field. These accolades reflect Dr. Silva’s dedication to advancing civil engineering through innovative research and professional service.

Research Focus  

Dr. Pedro Franco Silva’s research focuses on the development of innovative design procedures for civil structures to withstand man-made and natural hazards. His current projects include creating new interface configurations for unbonded post-tensioned shear walls that dissipate energy through contact friction while remaining damage-free. He is also advancing state-of-the-art seismic design practices for assessing and designing reinforced concrete slender bridge columns. Additionally, Dr. Silva’s research explores stochastic methodologies to quantify the probability of bridge collapse due to heavy truck collisions and subsequent fires from flammable vapors with fast heating rates. Through his pioneering research, Dr. Silva aims to enhance the resilience and safety of civil structures, contributing to the field’s understanding of structural behavior under extreme conditions.

Publication Top Notes

Seismic response of sacrificial shear keys in bridge abutments

Cyclic crack monitoring of a reinforced concrete column under simulated pseudo-dynamic loading using piezoceramic-based smart aggregates

Development of a performance evaluation database for concrete bridge components and systems under simulated seismic loads

Improving the blast resistance capacity of RC slabs with innovative composite materials

Rehabilitation of steel bridge members with FRP composite materials