Short Course | Structural Health Monitoring | Brazil

A 20-hour blended short course designed to pose the structural health monitoring (SHM) in the context of a statistical pattern recognition paradigm to support the damage identification process and risk-informed integrity management. This course is under preparation and will be organized in Curitiba, Brazil, in October 2025.

The remote component introduces the concept of SHM and accelerates the learning process, while the in-person component focuses on bridging the gap between research and practical application. The techniques are shown with hands-on experiences applied to bridges. Unsupervised learning techniques as Gaussian mixture Models; supervised learning algorithms like artificial neural networks or support vector machines; and transfer learning methods as the transfer component analysis are all covered. The role of SHM to support the climate change adaptation of bridges is also discussed. 

Target public: The course is tailored towards graduate students and/or practicing engineers working full-time in public and private institutions, like authorities and contractors.

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 by along the same lines facilitating the development and operation of sustainable energy infrastructure, like wide turbines and dams.

ENROLLMENT

Cost of attending the training has not been defined yet. 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 April 2025.

SPECIFIC OBJECTIVES

• Pose the SHM in the context of a statistical pattern recognition paradigm.
• Understand the differences between quasi-static and dynamic monitoring.
• Overview of sensors and DAQ hardware for designing an optimum 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 climate change adaptation.
• Pose the concept of probabilistic digital twins in the context of SHM.
• Understand the role of SHM to support risk-informed integrity management.
• Understand the goal of SHM with current limitations, grand challenges, and future trends.

DURATION, LOCATION, DATE, AND TIME

Duration: 20 hours

Remote component (4h)

In-person component (16h):

• Location: Curitiba, Brazil
• Date: October 27-28, 2025
• Time: 9:00 am to 18:00 (one hour for lunch)

COURSE SYLLABUS

Session #1 – October 2025 | Remote | Introduction to SHM in the context of a statistical pattern recognition paradigm (1h)
Session #2 – October 2025 | Remote | Monitoring technologies (2h)
Session #3 – October 27, 2025 (09-11h, Brasília Time) | in-person | Statistical pattern recognition paradigm for SHM (2h)
Session #4 – October 27, 2025 (11-13h, Brasília Time) | In-person | Mathematical foundations for feature extraction (2h)
Session #5 – October 27, 2025 (14-16h, Brasília Time) | In-person | Linear and nonlinear damage effects (2h)
Session #6 – October 27, 2025 (16-18h, Brasília Time) | In-person | Foundations of probabilistic FE modeling (2h)
Session #7 – October 28, 2025 (09-11h, Brasília Time) | in-person | SHM in Action: Unsupervised machine learning (2h)
Session #8 – October 28, 2025 (11-13h, Brasília Time) | In-person | SHM in Action: Supervised machine learning (2h)
Session #9 – October 28, 2025 (14-16h, Brasília Time) | In-person | Probabilistic digital twins in SHM (2h)
Session #10 – October 27, 2025 (16-18h, Brasília Time) | In-person | Risk-informed use of SHM (2h)
Session #11 – October 2025 | Remote | Hot Topics. Limitations, grand challenges, and trends (1h)

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: Este endereço de email está protegido contra piratas. Necessita ter o JavaScript autorizado para o visualizar.