Vasileios Ntertimanis: Catalogue data in Spring Semester 2020

Name Dr. Vasileios Ntertimanis
Strukturmechanik und Monitoring
ETH Zürich, HIT G 42.2
Wolfgang-Pauli-Str. 27
8093 Zürich
Telephone+41 44 633 79 45
DepartmentCivil, Environmental and Geomatic Engineering

101-0008-00LStructural Identification and Health Monitoring3 credits2GE. Chatzi, V. Ntertimanis
AbstractThis course will present methods for assessing the condition of structures based on monitoring. The term "monitoring" corresponds to measurements of structural response (e.g. strains, deflections, accelerations), which are nowadays available from low-cost and easily deployed sensor technologies. We show how to exploit sensing technology for maintaining a safe and resilient infrastructure.
ObjectiveThis course aims at providing a graduate level introduction into the identification and condition assessment of structural systems.

Upon completion of the course, the students will be able to:
1. Test Structural Systems for assessing their condition, as this is expressed through stiffness
2. Analyse sensor signals for identifying characteristic structural properties, such as frequencies, mode shapes and damping, based on noisy or incomplete measurements of the structural response.
3. Establish relationships governing structural response (e.g. dynamics equations)
4. Identify possible damage into the structure by picking up statistical changes in the structural "signature" (behavior)
ContentThe course will include theory and algorithms for system identification, programming assignments, as well as laboratory and field testing, thereby offering a well-rounded overview of the ways in which we may extract response data from structures.

The topics to be covered are :

1. Fundamentals of dynamic analysis (vibrations)
2. Fundamentals of signal processing
3. Modal Testing for determining the modal properties of Structural Systems
4. Parametric & Nonparametric Identification for processing test and measurement data
i) in the frequency domain (Spectral Analysis, Frequency Domain decomposition)
ii) in the time domain (Autoregressive models, the Kalman Filter)
5. Damage Detection via Stochastic Methods

A comprehensive series of computer/lab exercises and in-class demonstrations will take place, providing a "hands-on" feel for the course topics.

The final grade will be obtained, either
- by 30% from the graded exercises and 70% from the written session examination, or
- by the written session examination exclusively.
The highest ranking of the above two options will be used, so that assignments are only used to strengthen the grade.
Lecture notesThe course script is composed by the lecture slides, which are available online and will be continuously updated throughout the duration of the course:
LiteratureSuggested Reading:
T. Söderström and P. Stoica: System Identification, Prentice Hall International:
Prerequisites / NoticeFamiliarity with MATLAB is advised.
101-0190-08LUncertainty Quantification and Data Analysis in Applied Sciences Information
The course should be open to doctoral students from within ETH and UZH who work in the field of Computational Science. External graduate students and other auditors will be allowed by permission of the instructors.
3 credits4GE. Chatzi, P. Koumoutsakos, S. Marelli, V. Ntertimanis, K. Papadimitriou
AbstractThe course presents fundamental concepts and advanced methodologies for handling and interpreting data in relation with models. It elaborates on methods and tools for identifying, quantifying and propagating uncertainty through models of systems with applications in various fields of Engineering and Applied science.
ObjectiveThe course is offered as part of the Computational Science Zurich (CSZ) ( graduate program, a joint initiative between ETH Zürich and University of Zürich. This CSZ Block Course aims at providing a graduate level introduction into probabilistic modeling and identification of engineering systems.
Along with fundamentals of probabilistic and dynamic system analysis, advanced methods and tools will be introduced for surrogate and reduced order models, sensitivity and failure analysis, parallel processing, uncertainty quantification and propagation, system identification, nonlinear and non-stationary system analysis.
ContentThe topics to be covered are in three broad categories, with a detailed outline available online (see Learning Materials).
Track 1: Uncertainty Quantification and Rare Event Estimation in Engineering, offered by the Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Bruno Sudret, Dr. Stefano Marelli
Track 2: Bayesian Inference and Uncertainty Propagation, offered the by the System Dynamics Laboratory, University of Thessaly, and the Chair of Computational Science, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Georgios Arampatzis, Prof. Dr. Petros Koumoutsakos
Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis
The lectures will be complemented via a comprehensive series of interactive Tutorials will take place.
Lecture notesThe course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website.
LiteratureSuggested Reading:
Track 2 : E.T. Jaynes: Probability Theory: The logic of Science
Track 3: T. Söderström and P. Stoica: System Identification, Prentice Hall International, Link see Learning Materials.
Xiu, D. (2010) Numerical methods for stochastic computations - A spectral method approach, Princeton University press.
Smith, R. (2014) Uncertainty Quantification: Theory, Implementation and Applications SIAM Computational Science and Engineering,
Lemaire, M. (2009) Structural reliability, Wiley.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008) Global Sensitivity Analysis - The Primer, Wiley.
Prerequisites / NoticeIntroductory course on probability theory
Fair command on Matlab