Short Course 6h | Structural Health Monitoring

This in-person 1-day 6-hour 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, before the International Workshop on Structural Health Monitoring (IWSHM) 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. In 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.

Sustainable development goals: This course contributes to the Sustainable Development Goals (SDGs) 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.

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

ENROLLMENT

Cost of attending the training: €300. The registration is mandatory to prepare material, receipts, certificates, and coffee-breaks. A registration link will be made available soon. Meanwhile, we invite you to fill a registration form as a pre-enrollment to express your interest. We will notify you when official enrollment becomes available in February 2025.

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 role of SHM to support climate change adaptation.
• Understand the goal of SHM, with current limitations, grand challenges, and future trends.

COURSE SYLLABUS

OBSERVATIONS

• Each regular 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.
• A Certificate of Attendance will be issued at the end of the course.
• Contact information? Please email either of the instructors: Este endereço de email está protegido contra piratas. Necessita ativar o JavaScript para o visualizar. or Este endereço de email está protegido contra piratas. Necessita ativar o JavaScript para o visualizar.