Liang Luo| Civil Engineering | Best Scholar Award

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Dr. Liang Luo| Civil Engineering | Best Scholar Award

Luo Liang is a dedicated PhD student at the School of Civil Engineering, Harbin Institute of Technology, China. His work integrates cutting-edge research in structural engineering, focusing on bridge performance, concrete innovation, and seismic resilience. With a Master’s degree in Engineering and a robust academic trajectory, he has published 18 peer-reviewed articles, reflecting his deep expertise in structural stress analysis, recycled concrete materials, and bridge operation decision-making. Luo’s contributions span from experimental investigations to advanced finite element modeling, aiming to enhance infrastructure durability and sustainability. His research is both practical and forward-thinking, addressing challenges in modern construction through innovative material modification techniques such as nano-silica and mixed fibers. As a rising scholar in civil engineering, he demonstrates strong analytical skills, experimental precision, and a commitment to sustainable urban development, making notable impacts in the field of bridge engineering and green construction technologies.

Profile

🎓 Education

Luo Liang earned his Master of Engineering with a concentration in structural engineering, where he built a strong foundation in material mechanics, design principles, and infrastructure systems. Currently pursuing a PhD at the School of Civil Engineering, Harbin Institute of Technology (HIT), China, he is engaged in rigorous academic research that combines theoretical modeling with real-world applications. HIT, a prestigious institution ranked among China’s top engineering universities, provides Luo access to advanced laboratories and interdisciplinary collaboration. His doctoral studies focus on developing sustainable construction materials, evaluating seismic behavior of bridge structures, and decision-making for bridge operation and maintenance. Through his coursework, research projects, and collaborative publications, Luo has cultivated advanced knowledge in concrete technology, stress analysis, and infrastructure resiliency. His educational journey reflects his deep interest in structural innovation and environmental sustainability, with academic milestones consistently marked by technical excellence and research productivity in peer-reviewed platforms.

🧪 Experience

As a PhD researcher at Harbin Institute of Technology, Luo Liang brings several years of intensive academic and experimental experience in structural engineering. His work emphasizes hands-on analysis of bridge performance, material enhancement, and stress-state behavior under dynamic loads. Luo has co-authored 18 peer-reviewed research papers, collaborating with prominent scholars in experimental and numerical studies, including investigations on ultra-high-performance concrete (UHPC) and recycled aggregate concrete (RAC). His expertise extends to finite element modeling, ensemble learning methods for seismic performance prediction, and structural behavior simulations under vehicle-bridge interaction (VBI). Luo’s involvement in laboratory testing and real-world case studies has shaped his comprehensive understanding of civil infrastructure performance and control. Beyond technical execution, he has contributed to multidisciplinary research teams focused on resilience and sustainability in transportation systems. Through consistent innovation and application-driven research, Luo has emerged as a critical thinker and proactive contributor to the civil engineering research community.

🏅 Awards and Honors

While specific awards are not listed, Luo Liang’s academic recognition is evident in his publication record and research influence. With 18 peer-reviewed journal articles and over 2,900 citations (as of June 2025), his scholarly output speaks to the impact and quality of his contributions. He has been instrumental in projects that received institutional and departmental support for sustainable construction research, seismic performance analysis, and intelligent bridge monitoring. His high citation count and authorship in impactful studies on UHPC, recycled materials, and bridge seismic resilience suggest consistent acknowledgment by the global research community. Luo’s inclusion in collaborative research with well-established authors and reputable journals implies a strong academic reputation. As he advances in his career, he is likely to receive further accolades tied to green engineering, infrastructure resilience, and computational modeling. His rapid academic progression and scholarly productivity place him on a promising trajectory for future national and international recognition.

🔬 Research Focus

Luo Liang’s research focuses on advancing the durability, sustainability, and safety of civil infrastructure. His core interests include bridge operation and maintenance decision-making, structural stress-state analysis, seismic performance of reinforced concrete (RC) and steel-concrete composite bridges, and advanced concrete material modification. He investigates the mechanical properties and microstructural behavior of recycled aggregate concrete (RAC), enhanced using nano-silica and mixed fibers, aiming to improve emergency repair performance and reduce environmental impact. Luo also explores the dynamic interaction between vehicles and bridge systems (VBI), developing surrogate models using ensemble learning to predict seismic behavior more accurately. His methodological approach blends experimental techniques with finite element simulations, offering insights into load-bearing mechanisms and performance optimization. By addressing challenges in aging infrastructure and sustainability, Luo’s work contributes to safer, greener, and smarter structural systems, aligning with global priorities in civil engineering and disaster resilience. His research holds high relevance for both academia and practical infrastructure policy.

✅ Conclusion

Luo Liang is a promising structural engineering researcher whose work in sustainable materials, seismic modeling, and bridge maintenance significantly contributes to resilient and eco-efficient infrastructure systems, making him a valuable asset to the global civil engineering community.

Publications

 

 

 

 

Elyas Rostami | Mechanical Engineering | Best Researcher Award

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Dr. Elyas Rostami | Mechanical Engineering | Best Researcher Award

Dr. Elyas Rostami is a distinguished faculty member at Buein Zahra Technical and Engineering University, specializing in Aerospace Engineering 🚀. With a solid academic background, he earned his B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, followed by an M.Sc. in Aerospace Engineering ✈️ from Tarbiat Modares University, and completed his PhD 🎓 at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles 📄, books 📚, and research projects 🔍 to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization 💧, fluid mechanics 🌊, and sustainable energy applications ⚡. As a dedicated educator 👨‍🏫 and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations 🤝, publishing, and mentoring.

Profile

Education 🎓

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, laying the foundation for his technical expertise. He pursued his M.Sc. in Aerospace Engineering ✈️ at Tarbiat Modares University, where he refined his knowledge in aerodynamics, propulsion, and fluid mechanics 🌬️. Driven by academic excellence, he earned his Ph.D. in Aerospace Engineering 🚀 from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems 🔥, and energy-efficient solutions 💡. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems 🔧. Through rigorous coursework, experimental research, and publications 📝, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠💪, enabling him to address modern aerospace challenges with innovative thinking 🚀.

Experience 👨‍🏫

Dr. Elyas Rostami serves as a faculty member 👨‍🏫 at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering 🚀. His career spans academic teaching 📖, research leadership 🔬, article peer reviewing 🧐, and supervising research projects 🔍. He has authored multiple scientific papers 📝 and books 📚, advancing the field of propulsion systems, spray technology 💧, and fluid mechanics 🌊. Beyond academia, Dr. Rostami actively engages with industry via research collaborations 🤝 and applied projects that explore new energies 🌱 and aerospace applications ✈️. His professional journey demonstrates versatility and dedication, contributing both as an educator and innovator 🎓⚙️. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions 🏅

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction 🎖️. His scholarly contributions earned him recognition through research publications in reputable journals 🏅, invitations to review articles 🧐, and participation in collaborative research projects 🤝. He has authored a scientific book 📘 and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion 🔥 and fluid dynamics 🌊 has positioned him as a valued academic and research professional. The depth of his work reflects both national and international acknowledgment, solidifying his reputation as a committed and impactful researcher 🌍. Dr. Rostami’s work embodies passion for engineering, teaching excellence, and scientific advancement 💡🔬.

Research Interests 🔬

Dr. Elyas Rostami’s research focuses on fluid mechanics 🌊, aerospace propulsion systems 🚀, spray and atomization processes 💧, and the use of renewable energies in industrial applications 🌱. His investigations address the thermodynamics of propulsion systems 🔥, advancing efficiency and performance in aerospace technology ✈️. He integrates experimental and computational approaches 🖥️ to explore new energy solutions and optimize spray behavior under different conditions ⚙️. Through research projects 🔬, journal articles 📄, and book authorship 📚, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation 💡, making significant strides in the development of sustainable and high-performance propulsion technologies 🌍🚀.

Publications 

Lichen Shi | Mechanical Engineering | Best Researcher Award

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Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

 

Profile

Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

  • Developing an AI-based method for reading pointer meters using human-like reading sequences.
  • Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
  • Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
  • Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
  • Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

  • Intelligent Measurement with Deep Learning
  • Equipment Status Monitoring and Fault Diagnosis
  • Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

  • [1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
  • [2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) ：25186-25196. doi:
    10.1109/JSEN.2024.3417231
  • [3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
    Diagnosis of Rotating Machinery[J]. IEEE Sensors Journal, 2024,12:34785-34799. DOI:
    10.1109/access.2024.3368755.
  • 4] Haitao Wang, Mingjun Li, Zelin Liu, Xiyang Dai, Ruihua Wang and Lichen Shi*. RotaryMachinery Fault Diagnosis Based on Split Attention MechanismandGraphConvolutional Domain Adaptive Adversarial Network[J]. IEEE Sensors Journal, 2024,
    24(4) :5399-5413. doi: 10.1109/JSEN.2023.3348597.
  • [5] Haitao Wang, Xiyang Dai, Lichen Shi*. Gearbox Fault Diagnosis Based onMixedData-Assisted Multi-Source Domain Transfer Learning under Unbalanced Data[J]. IEEESensors Journal. doi: 10.1109/JSEN.2024.3477929