Fucai Li | Structural Health Monitoring and Fault Diagnosis | Best Researcher Award

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Dr. Fucai Li | Structural Health Monitoring and Fault Diagnosis | Best Researcher Award

Prof. Fucai Li is a distinguished academic at Shanghai Jiao Tong University 🇨🇳, specializing in vibration and ultrasonic signal processing, structural health monitoring, and intelligent sensor systems 🔍. With extensive global research experience across China 🇨🇳, Japan 🇯🇵, and Australia 🇦🇺, he has significantly contributed to mechanical engineering through innovative diagnostics and smart material systems 🛠️. He has published over 150 peer-reviewed papers and led numerous national and industrial research projects 📚🔬. His work bridges academia and industry, with collaborations involving giants like Bao Steel and Shanghai Electric ⚙️.

Profile

Education 🎓

Prof. Li earned his Ph.D. in Engineering from Xi’an Jiaotong University in 2003 🎓, where he also completed his Bachelor’s degree in Mechanical Engineering in 1998 🏫. His strong academic foundation from one of China’s premier technical universities laid the groundwork for a career focused on high-impact research 🧠. Throughout his education, he developed core expertise in mechanics, signal processing, and automation, setting the stage for innovations in sensor technologies and structural diagnostics 📡🔧.

Experience 👨‍🏫

Prof. Li currently serves as Professor at Shanghai Jiao Tong University (2015–present) 🏫. He was an Associate Professor there from 2009–2015 and held earlier roles including Assistant Professor (2003–2005) 🎓. His international experience includes research fellowships in Japan (JSPS, University of Tokyo, 2007–2009) 🇯🇵 and Australia (University of Sydney, 2005–2007) 🇦🇺. This global exposure enriched his expertise in structural innovation and smart systems 🌐. Across academia, he has shaped future engineers and researchers with cutting-edge knowledge and practical application insights 📘🔍.

Awards & Recognitions 🏅

Prof. Li has secured over 30 research grants from top national bodies like NSFC, Ministry of Science and Technology, and major industries including Bao Steel and Shanghai Electric 💼💡. He is widely recognized for his pioneering work in structural health monitoring and has published more than 150 influential journal papers 📑. His contributions to smart sensor networks and machine diagnostics have earned him national acclaim and trust from key industrial stakeholders 🤝. Through sustained innovation and impact, he has built a legacy of excellence in engineering research and collaboration 🏅.

Research Interests 🔬

Prof. Li’s research targets intelligent diagnostics and health monitoring of mechanical systems 🤖. His focus areas include vibration and ultrasonic signal analysis, fiber optic and piezo-electric sensor networks, and AI-driven fault detection systems 📊📡. He develops smart sensing technologies for predictive maintenance and infrastructure resilience, with applications ranging from aerospace to heavy machinery 🏗️✈️. His work integrates signal processing, materials science, and systems engineering to enable next-gen monitoring solutions. With over 150 publications and extensive funding, his research continues to push the frontier of smart mechanical engineering 🚀.

Publications 

Partha Sengupta | Structural Health Monitoring | Best Researcher Award

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Dr.Partha Sengupta | Structural Health Monitoring | Best Researcher Award

 

AECOM,India

Profile

Education

He holds a Ph.D. in Civil Engineering from the Indian Institute of Engineering Science and Technology (IIEST), Shibpur (2018–2023), with a perfect CGPA of 10/10. His doctoral research focused on “Finite Element Model Updating of Structures in Bayesian Framework and Enhanced Model Reduction Techniques.” Prior to this, he completed his M.Tech in Civil Engineering from IIEST, Shibpur (2014–2016), earning a CGPA of 9.03/10, with a thesis on the “Application of Ground Penetrating Radar in Concrete Evaluation, Pavement Profile, and Utility Detection.” He obtained his B.Tech in Civil Engineering from West Bengal University of Technology (2010–2014) with a CGPA of 9.02/10.

Professional Experience

Dr. Partha Sengupta conducts research in Structural Health Monitoring, focusing on model updating within a Bayesian framework using an enhanced model reduction technique with incomplete modal and time history response data. His work involves developing an iterative model reduction technique in the frequency domain by eliminating stiffness terms from the transformation equation, effectively mapping the full model and predicting its dynamic responses. The modified equation depends on measured modal responses and invariant mass matrices, eliminating the need for repeated evaluations of stiffness terms typically required in structural health monitoring (SHM) updating algorithms. Furthermore, this model reduction approach is integrated with a sub-structuring scheme, making it applicable to large finite element models. Additionally, Dr. Sengupta has developed an improved Bayesian model updating technique within the Transitional Markov Chain Monte Carlo (TMCMC) framework in the frequency domain, incorporating modifications to enhance the TMCMC algorithm.

AWARDS & ACHIEVEMENTS:

He received the Professor Amiya K. Basu Research Award in Structural Dynamics from the Department of Civil Engineering, IIEST Shibpur, and an additional monthly stipend of ₹10,000 from MHRD in 2022, along with his institute fellowship. He was also awarded the Best Paper Award in “Control and Health Monitoring” at the International Conference on Materials, Mechanics, and Structures (ICMMS 2020) in Kozhikode, India. As a reviewer, he has contributed to the G20 C20 International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development, published by Springer Nature, as well as Engineering Structures and Computer Methods in Applied Mechanics and Engineering, prestigious SCI journals published by Elsevier. Additionally, he received the Ministry of Human Resource and Development (MHRD) Institute Fellowship for pursuing his Ph.D. and M.Tech, having qualified GATE 2014 with a 99 percentile.

Publications