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This in-person 1-Day Short Course poses the structural health monitoring (SHM) applied to bridges and special civil structures in the context of a pattern recognition paradigm with hands-on experiences. It will be held at Stanford University, Palo Alto, California, in 2025.

Why bridges rather than just special structures? Because bridges are considered the main vulnerable civil structure in the last decades due to the number of structural failures observed around the world. Why SHM rather than SHM of bridges? Because most of the techniques and general procedures described are independent of the structure. Therefore, the techniques are first presented for general applications and then real examples are shown with hands-on experiences in the context of bridges. This manner, students can learn the general concept of SHM and apply it later on to almost any engineering structure.

Targeted public: The course is tailored towards graduate students and/or practicing engineers working full-time in public and private institutions or consultancy companies.

Keywords: SHM, bridges, pattern recognition, machine learning, damage identification, supervised learning, unsupervised learning, finite element modeling and numerical models.

ENROLLMENT

The registration form will be available soon. The registration is mandatory in order to prepare material, receipts, certificates, and coffee-breaks.

SPECIFIC OBJECTIVES

  • Pose the SHM in the context of a statistical pattern recognition paradigm.
  • Conduct damage identification using vibration-based SHM.
  • Understand the applicability of finite element modeling and machine learning for data interpretation and damage identification.
  • Give hands-on experiences to speed up the learning process on SHM, running application examples in real-time.
  • Understand the goal of SHM, with current limitations, grand challenges, and future trends.

COURSE SYLLABUS

Session #1 – 1.5h | Introduction to SHM and statistical pattern recognition paradigm for SHMSession #2 – 1.5h | SHM in Action: Data-based approach to SHM (unsupervised learning)Session #3 – 1.5h | Hybrid approach to SHM (based on modeling and monitoring data)Session #4 – 1.5h | The role of transfer learning for SHM | SHM for bridge adaptation to climate change | Limitations, grand challenges, and trends

OBSERVATIONS

  • Each session has a duration of 1.5 hours.
  • Course notes as well as codes written in Matlab will be distributed at the end of the course.
  • Certificate of Attendance will be issue at the end of the course.
  • Contact information? Please email either of the instructors: eloi.figueiredo@ulusofona.pt or dragos.moldovan@ulusofona.pt

INSTRUCTORS

Eloi Figueiredo – PhD in Civil Engineering (2010) and Full Professor at Lusófona University with over 110 publications on structural health monitoring (SHM) through books, book chapters, peer-reviewed journals, and conference proceedings; and about 90 opinion articles to promote science in our society. He is the coordinator of the Civil Research Group and has scientific collaborations with several institutions in Europe, United States, and Brazil.

Ionut Moldovan – PhD in Civil Engineering (2008), has more than 60 scientific publications, including books, book chapters and papers in international journals and conferences. He is the Principal Investigator of the Project CEN-DynaGeo, 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.

Course Website
Please visit the below website for more information
Short Course on Structural Health Monitoring