Open Courses Structural Health Monitoring - France
A 12-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. The remote part introdu Read more
Course formatMixed
Hours12h
Credits2 ECTS
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What will I learn in this course?
What will I learn in this course?
A 12-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. The remote part introduces the concept of SHM and accelerates the learning process, while the in-person part 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.
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Main reasons for choosing this course on ULusófona
Main reasons for choosing this course on ULusófona
- 1. Gain a strong foundation in SHM theory and practice.
- 2. Learn skills directly relevant to infrastructure safety and resilience.
- 3. Professional accreditation and broad applicability.
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Entry requirements and conditions
Entry requirements and conditions
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Technical and professional skills that I will acquire
Technical and professional skills that I will acquire
After successful completion of this course, students will be capable to: - Describe the historical and current real-world applications of damage identification in the civil engineering field, especially in bridges; - Conduct damage identification using vibration-based SHM; - Analyze and understand 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; - Evaluating critically the results of system and damage identification for quality control; - Understand and apply commercial software for system and damage identification analysis.
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Profile of the candidates for whom the course is intended
Profile of the candidates for whom the course is intended
The course is tailored towards graduate students and/or practicing engineers working full-time in public and private institutions, like authorities and contractors.
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Program
Program
- [Anual]
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Short Course on Structural Health Monitoring - France
2 ects
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Career opportunities and career prospects
Career opportunities and career prospects
1. Infrastructure Monitoring Engineer / Consultant 2. Asset Management and Resilience Specialist 3. Research and Innovation Roles (R&D / Digital Engineering)
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Scientific areas of the course
Scientific areas of the course
Engenharia e técnicas afins
Lisboa | Faculdade de Engenharia





