Eleni Chatzi: Katalogdaten im Frühjahrssemester 2022

NameFrau Prof. Dr. Eleni Chatzi
LehrgebietStrukturmechanik und Monitoring
Adresse
Inst. f. Baustatik u. Konstruktion
ETH Zürich, HIL E 33.3
Stefano-Franscini-Platz 5
8093 Zürich
SWITZERLAND
Telefon+41 44 633 67 55
Fax+41 44 633 10 64
E-Mailchatzi@ibk.baug.ethz.ch
URLhttp://www.chatzi.ibk.ethz.ch/
DepartementBau, Umwelt und Geomatik
BeziehungAusserordentliche Professorin

NummerTitelECTSUmfangDozierende
101-0008-00LStructural Identification and Health Monitoring3 KP2GE. Chatzi, V. Ntertimanis
KurzbeschreibungThis 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.
LernzielThis 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
InhaltThe 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.
SkriptThe 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
LiteraturSuggested Reading:
T. Söderström and P. Stoica: System Identification, Prentice Hall International: http://user.it.uu.se/~ts/sysidbook.pdf
Voraussetzungen / BesonderesFamiliarity with MATLAB is advised.
101-0114-00LBaustatik II Information 5 KP5GE. Chatzi
KurzbeschreibungDiese Vorlesung bietet die Grundlage für fortgeschrittene Überlegungen zur Strukturanalyse. Dazu gehören die Lösung unbestimmter Systeme mit Hilfe der Verformungsmethode und der Matrizenstrukturanalyse (Direkte Steifigkeits Methode) sowie die Lösung von Systemen mit nichtlinearem Materialverhalten (z.B. aufgrund von Plastizität).
LernzielBeherrschen der Methoden zur Berechnung statisch unbestimmter Stabtragwerke
Erweiterung des Verständnisses des Tragverhaltens von Stabtragwerken unter Einbezug Plastizitätseffekte
Fähigkeit, Resultate numerischer Berechnungen vernünftig zu interpretieren und zu kontrollieren
InhaltLineare Statik der Stabtragwerke
Verformungsmethode
Matrizenstatik

Nichtlineare Statik der Stabtragwerke
Elastisch-plastische Systeme
Traglastverfahren
LiteraturSimon Zweidler, "Baustatik II", 2017.
Peter Marti, "Baustatik", Wilhelm Ernst & Sohn, Berlin, 2012, 683 pp.
Voraussetzungen / BesonderesVoraussetzung: "Baustatik I"
101-0158-01LMethod of Finite Elements I Information 5 KP3GE. Chatzi, P. Steffen
KurzbeschreibungThe course introduces students to the fundamental concepts of the Method of Finite Elements, including element formulations, numerical solution procedures and modelling details. We aim to equip students with the ability to code algorithms (based on Python) for the solution of practical problems of structural analysis.
DISCLAIMER: the course is not an introduction to commercial software.
LernzielThe Direct Stiffness Method is revisited and the basic principles of Matrix Structural Analysis are overviewed.
The basic theoretical concepts of the Method of Finite Elements are imparted and perspectives for problem solving procedures are provided.
Linear finite element models for truss and continuum elements are introduced and their application for structural elements is demonstrated.
The Method of Finite Elements is implemented on practical problems through accompanying demonstrations and assignments.
Inhalt1) Introductory Concepts
Matrices and linear algebra - short review.

2) The Direct Stiffness Method
Demos and exercises in Python

3) Formulation of the Method of Finite Elements.
- The Principle of Virtual Work
- Isoparametric formulations
- 1D Elements (truss, beam)
- 2D Elements (plane stress/strain)
Demos and exercises in Python

4) Practical application of the Method of Finite Elements.
- Practical Considerations
- Results Interpretation
- Exercises, where structural case studies are modelled and analyzed
SkriptThe lecture notes are in the form of slides, available online from the course webpage:
https://chatzi.ibk.ethz.ch/education/method-of-finite-elements-i.html
LiteraturStructural Analysis with the Finite Element Method: Linear Statics, Vol. 1 & Vol. 2 by Eugenio Onate (available online via the ETH Library)

Supplemental Reading
Bathe, K.J., Finite Element Procedures, Prentice Hall, 1996.
Voraussetzungen / BesonderesPrior basic knowledge of Python is necessary.
101-0190-08LUncertainty 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 KP4GE. Chatzi, P. Koumoutsakos, S. Marelli, V. Ntertimanis, K. Papadimitriou
KurzbeschreibungThe 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.
LernzielThis 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.
InhaltThe 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.
SkriptThe course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website.
LiteraturSuggested 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.
Voraussetzungen / BesonderesIntroductory course on probability theory
Fair command on Matlab
101-0522-10LDoctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
Number of participants limited to 21.
1 KP2SB. Soja, E. Chatzi, F. Corman, I. Hajnsek, K. Schindler
KurzbeschreibungCurrent research in machine learning and data science within the research fields of the department. The goal is to learn about current research projects at our department, to strengthen our expertise and collaboration with respect to data-driven models and methods, to provide a platform where research challenges can be discussed, and also to practice scientific presentations.
Lernziel- learn about discipline-specific methods and applications of data science in neighbouring fields
- network people and methodological expertise across disciplines
- establish links and discuss connections, common challenges and disciplinespecific differences
- practice presentation and discussion of technical content to a broader, less specialised scientific audience
InhaltCurrent research at D-BAUG will be presented and discussed.
Voraussetzungen / BesonderesThis doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar.

Participants are expected to possess elementary skills in statistics, data
science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects.
101-1187-00LKolloquium Baustatik und Konstruktion0 KP1KB. Stojadinovic, E. Chatzi, A. Frangi, W. Kaufmann, B. Sudret, A. Taras
KurzbeschreibungDas Institut für Baustatik und Konstruktion (IBK) lädt Professoren in- und ausländischer Hochschulen, Fachleute aus Praxis & Industrie oder wissenschaftliche Mitarbeiter des Institutes als Referenten ein. Das Kolloquium richtet sich sowohl an Studierende und weitere Hochschulangehörige, als auch an Ingenieure aus der Praxis.
LernzielNeue Forschungsergebnisse aus dem Fachbereich Baustatik und Konstruktion kennen lernen.