Eleni Chatzi: Catalogue data in Spring Semester 2022

Name Prof. Dr. Eleni Chatzi
FieldStructural Mechanics and Monitoring
Address
Inst. f. Baustatik u. Konstruktion
ETH Zürich, HIL E 33.3
Stefano-Franscini-Platz 5
8093 Zürich
SWITZERLAND
Telephone+41 44 633 67 55
Fax+41 44 633 10 64
E-mailchatzi@ibk.baug.ethz.ch
URLhttp://www.chatzi.ibk.ethz.ch/
DepartmentCivil, Environmental and Geomatic Engineering
RelationshipFull Professor

NumberTitleECTSHoursLecturers
101-0008-00LStructural Identification and Health Monitoring3 credits2GE. Chatzi, V. Ntertimanis
AbstractThis 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.
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 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
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. 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 notesThe 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
LiteratureSuggested Reading:
T. Söderström and P. Stoica: System Identification, Prentice Hall International: http://user.it.uu.se/~ts/sysidbook.pdf
Prerequisites / NoticeFamiliarity with MATLAB is advised.
101-0114-00LTheory of Structures II Information 5 credits5GE. Chatzi
AbstractThis course offers the foundation to advanced consideration for structural analysis. This includes the solution of indeterminate systems via use of the Deformation Method and Matric Structural Analysis, as well as the solution of systems with nonlinear material behavior (e.g. due to plasticity).
ObjectiveMastering the methods of analysis for statically indeterminate beam and frame structures
Extending the understanding of the response of beam and frame structures by accounting for plasticity effects
Ability to reasonably interpret and check the results of numerical analyses
ContentLinear analysis of beam and frame structures
Force (flexibility) method
Displacement (stiffness) method
Matrix analysis

Nonlinear analysis of beam and frame structures
Elastic - plastic systems
Limit (failure) analysis
LiteratureSimon Zweidler, "Baustatik II", 2017.
Peter Marti, "Theory of Structures", Wiley, 2013, 679 pp.
Prerequisites / NoticePrerequisite: "Theory of Structures I"
101-0158-01LMethod of Finite Elements I Information 5 credits3GE. Chatzi, P. Steffen
AbstractThe 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.
ObjectiveThe 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.
Content1) 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
Lecture notesThe 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
LiteratureStructural 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.
Prerequisites / NoticePrior 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 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.
ObjectiveThis 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.
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
101-0522-10LDoctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering Restricted registration - show details
Does not take place this semester.
Number of participants limited to 21.
1 credit2SB. Soja, E. Chatzi, F. Corman, I. Hajnsek, K. Schindler
AbstractCurrent 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.
Objective- 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
ContentCurrent research at D-BAUG will be presented and discussed.
Prerequisites / NoticeThis 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.
101-1187-00LColloquium Structural Engineering0 credits1KB. Stojadinovic, E. Chatzi, A. Frangi, W. Kaufmann, B. Sudret, A. Taras
AbstractProfessors from national and international universities, technical experts from private industry as well as research associates of the Institute of Structural Engineering (IBK) are invited to present recent research results and specific projects. The colloquium is addressed to students, academics as well as practicing engineers.
ObjectiveBecome acquainted with recent research results in structural engineering.