This special edition of the Short Course on Structural Health Monitoring (SHM), hosted by Tongji University in Shanghai, China, offers an intensive journey from the fundamental principles of SHM, within a statistical pattern recognition paradigm, to its implementation on bridges. Registration is now open!
Target public: The course is tailored towards graduate students and/or practitioners working full-time in public and private institutions, such as infrastructure owners, authorities, and contractors.
Keywords: SHM, bridges, structural mechanical modeling, pattern recognition, machine learning, system identification, damage identification, uncertainty, probability, probabilistic digital twins, risk, resilience, sustainability.
Accreditation: This short course has been accredited by Ordem dos Engenheiros - the Portuguese regulatory and licensing body for the engineering profession in Portugal.
Sustainable Development Goals: This course contributes to the Sustainable Development Goals (SDGs) 7, 9, and 13 by promoting sustainable and resilient infrastructure through the introduction of new technologies and innovation to guarantee the safety and comfort of people, through optimization of design and integrity management to reduce embedded CO2 and thereby countering climate change, and finally, along the same lines, facilitating the development and operation of sustainable energy infrastructure, like wind turbines and dams.
REGISTRATION
The cost of attending the training is €200 (two hundred Euros). The registration can be done here. Registration is mandatory to prepare materials, receipts, and certificates. If you have any problems, just send an email to
DURATION, LOCATION, DATE, AND TIME
- Duration: 12 hours (hybrid mode)
- Location: 201 Bridge Building, 1239 Siping, Tongji University, Shanghai 200092, China
- The link for remote participants will be sent later on
- Date: October 19 and 20, 2026
- Time: 14:00 to 21:00 China time (including one hour for a break)
SPECIFIC OBJECTIVES
- Pose SHM in the context of a statistical pattern recognition paradigm.
- Understand the differences between quasi-static and dynamic monitoring.
- Review sensors and DAQ hardware for designing an optimal instrumentation scheme for SHM.
- Understand the applicability of finite element modeling and machine learning (unsupervised and supervised learning) for data enhancement and interpretation, as well as for damage identification.
- Understand the role of SHM to support asset management.
- Understand the role of SHM to support climate change adaptation.
- Understand the role of SHM to support risk-informed integrity management.
- Understand the goal of SHM with current limitations, grand challenges, and future trends.
HIGHLIGHTS
After successful completion of this course, participants will be capable to:
- Describe the historical and current real-world applications of damage identification in the civil infrastructure, especially in bridges;
- Conduct damage identification using vibration-based SHM;
- Analyze the condition of data sets of monitoring results;
- Employ machine learning algorithms for system and damage identification;
- Develop an integrated application of machine learning and probabilistic digital twin in the context of SHM;
- Evaluate the results of system and damage identification for quality control;
- Apply commercial software for system and damage identification analysis.
REASONS TO ATTEND THIS COURSE
- Gain a strong foundation in SHM theory and practice
- Learn skills directly relevant to infrastructure safety and resilience
- Professional accreditation and broad applicability
PROFESSIONAL OPPORTUNITIES
- Infrastructure Monitoring Engineer / Consultant
- Asset Management and Resilience Specialist
- Research and Innovation Roles (R&D / Digital Engineering)
COURSE SYLLABUS
Session #2 – October 19, 2026 (15:30 - 17:00 China) | Hybrid |Statistical pattern recognition paradigm for SHM + Data-based training of machine learning algorithms for outlier detection (unsupervised learning)
Session #3 – October 19, 2026 (18:00 - 19:30 China) | Hybrid | Hybrid (monitoring- and physics-based) data training of machine learning algorithms for damage identification (supervised learning)
Session #4 – October 19, 2026 (19:30 - 21:00 China) | Hybrid | SHM in Action
Session #5 – October 20, 2026 (14:00 - 16:00 China) | Hybrid | Risk-informed integrity management
Session #6 – October 20, 2026 (16:00 - 17:00 China) | Hybrid | From monitoring to bridge SHM, including standards and codes in China
Session #7 – October 20, 2026 (18:00 - 19:00 China) | Hybrid | From bridge inspections to bridge management
Session #8 – October 20, 2026 (19:00 - 20:00 China) | Hybrid | Analysis of Bridge Collapses and vessel impacts in China
Session #9 – October 20, 2026 (20:00 - 21:00 China) | Hybrid | Trending SHM topics: The role of transfer learning for SHM; the role of SHM for climate change adaptation. Limitations, grand challenges, and trends (1.5h)
OBSERVATIONS
- The instructors reserve the right to modify the course organization as necessary to maintain the highest standards of content quality.
- Course notes will be distributed during the course.
- A Certificate of Attendance will be issued at the end of the short course.
- If you have any questions, please send us an email:
This email address is being protected from spambots. You need JavaScript enabled to view it. - Last update: October 8, 2026.
INSTRUCTORS
Eloi Figueiredo – PhD in Civil Engineering (2010) and Full Professor at the Faculty of Engineering at Lusófona University in Lisbon, Portugal. He is also a Visiting Scholar at Tongji University under the 2025 Shanghai Sci-tech Co-research Program (Grant No. 25HB2707400). He has dedicated his academic career to teaching structural engineering and conducting research in structural health monitoring (SHM) and the climate change adaptation of transport infrastructure. He has been actively involved in organizing international courses on SHM, fostering knowledge exchange and training researchers and practitioners. He is currently an Associate Editor of the Structural Health Monitoring journal and Chair of IABSE Task Group 5.11, which supports the transition of SHM from research to practice. He has authored more than 125 scientific publications, including books, book chapters, peer-reviewed journal articles, and conference papers.
Ye Xia – Dr. Ye Xia is an Associate Professor at Tongji University, Shanghai, China. He serves as Deputy Director of the Department of Bridge Engineering and Vice Chair of the Chinese Group of IABSE. He graduated from Tongji University and the University of California, Irvine, and worked as a Postdoctoral Associate at Rutgers University from 2011 to 2013. He has been a licensed Professional Engineer in California since 2013. His research focuses on structural health monitoring, bridge condition assessment, data-driven modeling, and AI-enabled infrastructure management. He has published over 140 SCI-indexed journal papers, authored two monographs, and holds more than 20 invention patents. He is a CNKI Highly Cited Scholar (TOP 1%) and recipient of the IABSE Early Career Prize, along with multiple science and technology awards.
Ionut Moldovan – PhD in Civil Engineering (2008) and Associate Professor at Lusófona University, has more than 100 scientific publications, including books, book chapters and papers in international journals and conferences. He is the Principal Investigator of the Project INTENT, funded by the Portuguese Science Foundation (FCT), and lead developer of FreeHyTE, the first public, open-source and user-friendly computational platform using hybrid-Trefftz finite elements.
Michael Havbor Faber – Professor at Lusófona University. He is discipline Director for Risk, Resilience and Sustainability at NIRAS A/S in Denmark, and he has a position as Chair Professor at Harbin Institute of Technology in China. His research interests are directed on probabilistic modeling and analysis of systems with applications to governance and management of risks, resilience and sustainability in the built environment. Initiating president of the Joint Committee on the GLOBE Consensus, past president of the Joint Committee on Structural Safety, member of the WEF Global Expert Network on Risk and Resilience, member of the Danish Research Council and the Danish Academy of Technical Sciences. Michael was awarded the Allin C. Cornell Award in 2019.