Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

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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.

Kangjian Yang | Trenchless technology | Best Researcher Award

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Dr. Kangjian Yang | Trenchless technology | Best Researcher Award

Lecturer I, Henan University, China.

Kangjian Yang is a distinguished lecturer at Henan University, specializing in disaster prevention and control as well as trenchless rehabilitation of underground pipelines. He holds a Ph.D. in Engineering and has established himself as a leading researcher in the field. With over 20 SCI-indexed publications and 6 patents, Yang’s work has had significant implications for the safety and sustainability of infrastructure systems. His research blends advanced methodologies, such as intelligent modeling and non-destructive testing, with practical applications to address issues in underground pipeline management. Through his contributions, Yang has enhanced the understanding of mechanical behavior in deteriorating infrastructure, particularly in concrete drainage and corroded pipes. His ongoing involvement in the National Key Research and Development Program of China underscores his commitment to advancing engineering practices and technology. His work has earned him recognition in the academic community, with an h-index of 14 and 305 citations to date.

Profile:

Scopus

Education:

Kangjian Yang completed his undergraduate studies in Civil Engineering before pursuing advanced studies in the same field. He earned a Master’s degree and subsequently a Ph.D. in Engineering, focusing on pipeline rehabilitation and disaster prevention technologies. His academic journey led him to work on cutting-edge technologies aimed at understanding and mitigating the impacts of environmental stressors on civil infrastructure, particularly in underground pipelines. During his doctoral studies, Yang honed his expertise in mechanical modeling, data-driven analysis, and rehabilitation techniques for aging infrastructure. Throughout his academic career, he has been involved in numerous research projects related to underground pipeline safety and maintenance, contributing significantly to the understanding of the mechanical properties of corroded pipes and trenchless technology. Yang’s education laid the foundation for his pioneering research on non-destructive testing and advanced rehabilitation techniques, which are now applied across various national and international projects.

Experience:

Kangjian Yang brings a wealth of experience in both academic and research settings. As a lecturer at Henan University, he has been actively engaged in teaching and mentoring the next generation of engineers. His expertise spans across underground pipeline assessment, non-excavation technology, and artificial intelligence in infrastructure management. In addition to his academic role, Yang has contributed to several high-profile industry projects, applying his research to real-world challenges in pipeline rehabilitation and disaster prevention. One of his notable contributions has been his involvement in the National Key Research and Development Program of China, where he leads projects focused on improving the safety and operational efficiency of underground pipeline systems. Yang’s collaborative approach extends to his involvement in consultancy projects with industrial partners, where he bridges the gap between theoretical research and practical application. His focus on practical, technology-driven solutions has made him an influential figure in the field of civil engineering.

Awards and Honors:

Kangjian Yang’s groundbreaking contributions to civil engineering have been widely recognized. Although specific awards are not listed, his work has earned him numerous accolades within the academic and professional communities. His publications in high-impact journals like Tunnelling and Underground Space Technology and Engineering Failure Analysis have garnered significant attention, further cementing his status as a leading researcher in underground pipeline assessment and rehabilitation. The citation of his work in over 300 documents and his h-index of 14 attest to his significant influence on the field. Yang’s patents, particularly in the domain of trenchless technology and pipeline rehabilitation, demonstrate his innovative approach to solving critical infrastructure challenges. His involvement in the National Key Research and Development Program of China highlights his leadership in advancing research that addresses national infrastructure issues. These achievements reflect his growing reputation and make him a strong candidate for the Best Researcher Award.

Research Focus:

Kangjian Yang’s research focuses primarily on the safety, assessment, and rehabilitation of underground pipelines, particularly through non-excavation technologies. His work addresses the challenges posed by aging infrastructure, environmental stress, and the mechanical properties of corroded pipes. Yang has pioneered the development of intelligent models that predict the behavior of defected concrete drainage pipes, offering more efficient solutions for maintaining underground systems. His research on trenchless rehabilitation techniques, such as Cured-In-Place Pipe (CIPP) and polymer grouting, has the potential to transform how aging and deteriorating infrastructure is managed. Yang’s use of artificial intelligence in the modeling and safety assessment of underground pipelines aims to optimize infrastructure management by considering complex environmental and load factors. He is also exploring non-destructive testing methods to better evaluate and monitor pipeline conditions. Overall, his work seeks to enhance the durability and safety of underground pipelines, addressing critical issues in civil engineering and infrastructure maintenance.

Publications:

  1. An intelligent model to predict the mechanical properties of defected concrete drainage pipes 📰
  2. Investigation of mechanical properties of corroded concrete pipes after cured-in-place-pipe (CIPP) rehabilitation under multi-field coupling 🔧
  3. Full-scale experimental investigation of the mechanical characteristics of corroded buried concrete pipes after cured-in-place-pipe rehabilitation 🏗️
  4. Mechanical evaluation analysis of pipe-liner composite structure before and after polymer grouting rehabilitation 🛠️
  5. Experimental study on the mechanical properties of corroded concrete pipes subjected to diametral compression 📊