

Yixian LI
The Hong Kong Polytechnic University
Dr. Yixian Li received his Ph.D. Degree from the Department of Bridge Engineering of Tongji University in 2021. He serves as the postdoc research fellow in the Department of Civil and Environmental Engineering of PolyU since 2022. His research interests include structural health monitoring, full-field sensing, physics-informed big data mining, and intelligent infrastructure management. He has published over 20 SCI-indexed papers with an H-index of 11. Dr. Li has received many international awards including the Best Oral Presentation of Hong Kong Scholars Annual Symposium, First Prize from Youth Forum Competition of MCCE2023, Excellent Doctoral Dissertation from Tongji University, National Scholarship, etc.

Zhipeng Zhao
Tongji University
Dr. Zhao obtained his Ph.D. in Civil Engineering from Tongji University (China) in 2021 and currently serves as an Assistant Professor at the College of Civil Engineering, Tongji University. His research focuses on intelligent disaster prevention and ground motion analysis. He has been recognized as a JSPS Foreign Research Fellow, with research appointments at prominent institutions such as The Hong Kong Polytechnic University and Tohoku University. Dr. Zhao has led four funded research projects, serves as Associate Editor for an international journal, and has published over 50 SCI-indexed papers along with 20 patents. His contributions are among the most-cited in Engineering Structures, and he is listed within the top 2% of scientists worldwide.

Xiaoyou Wang
The Hong Kong Polytechnic University
Dr. Wang received the B.S. Degree from Huazhong University of Science and Technology in 2015, M.S. degree from Tongji University in 2018, and Ph.D. degree from The Hong Kong Polytechnic University in 2021. Her PhD thesis, titled “Probabilistic machine learning and Bayesian inference for vibration-based structural damage detection”, received the Faculty Award for Outstanding PhD Thesis 2021/2022. She is currently a Research Assistant Professor at the Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University. Her current research interests include structural health monitoring and system identification, damage detection, Bayesian inference, probabilistic machine learning, quantum machine learning, and edge computing.

Yiming Zhang
Southeast University
Dr Yiming Zhang is a Young Chair Professor at Southeast University. He received his PhD degrees in Civil Engineering at Monash University (Australia) and Southeast University (China), followed by positions as a Postdoctoral Fellow at the Hong Kong Polytechnic University and a Marie Curie Fellow at the University of Cambridge where he was an undergraduate supervisor at Jesus College. His research focuses on probabilistic machine-learning methods for addressing engineering problems from a data-driven perspective. He also serves on the Early Career Editorial Board for Advances in Wind Engineering, Sustainable Structures, and the Journal of Highway and Transportation Research and Development. Prof. Zhang was a recipient of the Marie Curie Fellowship, Best Paper Awards, Key Scientific Articles in Advances in Engineering, First-Class Award of Science and Technology of China Highway Society, and Outstanding PhD thesis of Jiangsu Province. He leads several key projects, including the Excellent Young Scholars Fund (Overseas) from the National Natural Science Foundation (NSFC) of China, an EU Horizon 2020 program, the Cambridge University Trimble Fund, and the Hong Kong Polytechnic University Postdoctoral Match Fund.
Structural health monitoring (SHM) delivers crucial real-time information on the safety condition of existing structures, providing early warnings before catastrophic failures. With the aging and deterioration of civil infrastructures worldwide, automatic and intelligent monitoring techniques have become indispensable. However, SHM is a typical ill-posed problem and has undergone constant improvement in the past three decades.
This Methods Collection is designed to showcase a variety of intelligent SHM methods for advanced data mining, load-response analysis, damage identification, and health condition assessment. Submissions showcasing the latest research findings, technological developments, and successful applications of SHM in civil engineering are particularly welcome. We also encourage contributions that explore innovative methodologies, theoretical advancements, and practical implementations of SHM.
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2025
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1School of Civil Engineering, Shijiazhuang Tiedao University, 2Key Laboratory of Roads and Railway Engineering Safety Control, Shijiazhuang Tiedao University, 3Innovation Center of Wind Engineering & Wind Energy Technology Hebei, Shijiazhuang Tiedao University, 4School of Civil Engineering, Tongji University