universidade lusófona

Summer Course on Structural Health Monitoring

The main goal of this summer course is to introduce the concept of structural health monitoring (SHM) applied to bridges and special civil structures to MSc and PhD students from several European institutions.

Note that due to bureaucratic issues, this course hasn’t been confirmed yet.


5 ECTS | June 26-30, 2023, in person (30h) | 15h remotely | MSc and PhD Students


The SHM is posed in the context of a statistical pattern recognition paradigm, rooted in the artificial intelligence field, where machine learning algorithms are essential to perform damage identification, by learning (or modeling) the structural behavior from the experience (past data), following the same principle of the human brain. In order to balance the general concept and the applicability of SHM, this course has been designed to allow students to gain a basic knowledge of SHM with hands-on experiences to demonstrate how SHM can be used for assessing the condition of structural systems, along with the lectures to give some insight into the theory and application examples.


Keywords: Structural Health Monitoring, Bridges, Machine Learning, Finite Element Modeling, Digital Twins, Climate Change

Funding source? Erasmus+ Program through Blended Intensive Program (BIP), Student Mobility of Education (SME), and Staff Mobility for Teaching (SMT). The BIP covers expenses related to the course organization. SMT and SME cover expenses related to traveling and housing of students in Lisbon.

Which are the host and partners institutions? Lusófona University is host and responsible for the organization and funding of the course through BIP. Each partner institution is responsible to find students and staff members through SME and SMT (this funding must be coordinated with the international office of each partner institution).

Who can participate? Each institution can indicate up to four MSc and PhD students interested to speed up the learning process on SHM posed in the context of a statistical pattern recognition paradigm. Additionally, each institution can propose one senior researcher for lecturing (up to 3h).

Course material? PowerPoints, books, and research articles. All course material is available from a website in an e-learning platform (Moodle).

Learning outcomes:
  • 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.
  • Calibrate, optimize, and apply finite element model updating for deterministic and probabilistic approaches.
  • Choose the best method for damage identification as a function of the damage type.
  • Evaluating critically the results of damage identification for quality control.
  • Run damage identification analysis on commercial software, such as MATLAB and Robot.

Table 1. Course’s organization.

Activity Teaching mode Description Contact hours Date
1 Remote Welcome session 1 May, 2023
2 Remote Introduction to course topic – structural health monitoring 9 May-June, 2023
3 Remote Project’s presentation 1 June, 2023
4 In-person Lecturing and laboratory activities 30 26-30 June, 2023
5 Remote Student’s presentation of final project 3 July, 2023
6 Remote Closing session 1 July, 2023

Instructors:

  • Eloi Figueiredo, Lusófona University, Portugal
  • Ionut Moldovan, Lusófona University, Portugal
  • Arnaud Deraemaeker, Université Libre de Bruxelles, Belgium
  • Grzegorz Poprawa, Silesian University of Technology, Poland
  • Maria Pina Limongelli, Politecnico di Milano, Italy
  • Mihai Nedelcu, Technical University of Cluj-Napoca, Romania
  • Roland Kromanis, University of Twente, Netherlands

Notes:

  • A Transcript of Records and a Certificate of Attendance will be issue at the end of the course.
  • We have vacancies available, so for more information please contact Eloi Figueiredo: eloi.figueiredo@ulusofona.pt.
  • This is an international course that involves a lot of people, so there may be changes at any time. Last update: July 21, 2023.