Vasileios Ntertimanis: Catalogue data in Spring Semester 2022

Name	Dr. Vasileios Ntertimanis
Address	Strukturmechanik und Monitoring ETH Zürich, HIL E 33.2 Stefano-Franscini-Platz 5 8093 Zürich SWITZERLAND
Telephone	+41 44 633 79 45
E-mail	v.derti@ibk.baug.ethz.ch
Department	Civil, Environmental and Geomatic Engineering
Relationship	Lecturer

Number	Title	ECTS	Hours	Lecturers
101-0008-00L	Structural Identification and Health Monitoring	3 credits	2G	E. Chatzi, V. Ntertimanis
Abstract	This course will present methods for structural identification and health monitoring. We show how to exploit measurements of structural response (e.g. strains, deflections, accelerations) for evaluating structural condition, with the purpose of maintaining a safe and resilient infrastructure.
Learning objective	This 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 measurements of dynamic response. 2. Analyse vibration signals for identifying characteristic structural properties, such as frequencies, mode shapes and damping, based on noisy measurements of the structural response. 3. Formulate structural equations in the time and frequency domain 4. Identify possible damage into the structure by picking up statistical changes in the structural behavior
Content	The 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. Elements of Vibration Theory 2. Transform Domain Methods 3. Digital Signals (P 4. Nonparametric Identification for processing test and measurement data (transient, correlation, spectral analysis) 5. Parametric Identification (time series analysis, transfer functions) A series of computer/lab exercises and in-class demonstrations will take place, providing a "hands-on" feel for the course topics. Grading: - This course offers optional homework as learning tasks, which can improve the grade of the end-of-semester examination up to 0.25 grade points (bonus). - The learning tasks will be taken into account if all 3 homeworks are submitted. The maximum grade of 6 can also be achieved by sitting the final examination only.
Lecture notes	The course script is composed by the lecture slides, which are available online and will be continuously updated throughout the duration of the course: https://chatzi.ibk.ethz.ch/education/structural-identification-and-health-monitoring.html
Literature	Suggested Reading: T. Söderström and P. Stoica: System Identification, Prentice Hall International: http://user.it.uu.se/~ts/sysidbook.pdf
Prerequisites / Notice	Familiarity with MATLAB is advised.
101-0190-08L	Uncertainty Quantification and Data Analysis in Applied Sciences The is 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 credits	4G	E. Chatzi, P. Koumoutsakos, S. Marelli, V. Ntertimanis, K. Papadimitriou
Abstract	The 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.
Learning objective	This 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.
Content	The 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.
Lecture notes	The course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website.
Literature	Suggested 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 / Notice	Introductory course on probability theory Fair command on Matlab