Eleni Chatzi: Catalogue data in Autumn Semester 2021

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
RelationshipAssociate Professor

NumberTitleECTSHoursLecturers
101-0129-00LNon Destructive Evaluation & Rehabilitation of Existing Structures3 credits2GE. Chatzi, B. Herraiz Gómez, G. Kocur
AbstractIntroduction to non destructive evaluation tools and quantitative structural analyses and verifications for condition assessment of existing structures and subsequent decisions on their rehabilitation.
ObjectiveThe goal is for students to familiarize themselves with the handling of assessment and rehabilitation of existing structures from the perspective of a consulting engineer, following a systematic approach as described in current codes and to further learn how to use new non destructive evaluation technologies.
ContentThis course is organized in two main pillars. The first pillar describes the technologies that are available for non destructive evaluation of structures and delves into description of the principle of operation of such methods (e.g. wave propagation, acoustic emission analysis, tomography). The second pillar, overviews the current implementation of condition assessment processes in codes and standards. Complementary to the topic of structural evaluation, the topic of interventions, rehabilitation and retrofitting of existing structures for different construction materials is next addressed.
Lecture notesLecture notes
LiteratureJ. D. Achenbach, Wave propagation in elastic solids, North-Holland Publishing Company, 425p, 1973
J. L. Rose, Ultrasonic Guided Waves in Solid Media, Cambridge University Press, 506p, 2014
N. Ida and N. Meyendorf, Handbook of Advanced Nondestructive Evaluation, Springer, 1617p, 2019
Swiss Standards SIA 269, 269/1 to 269/7
SIA-Document D 0239 « Existing Structures – Introduction » (in German/French)
SIA-Document D 0239 « Existing Structures – Consolidation and Practice » (in German/French)
A. Costa, A. Arêde, H. Varum, Strengthening and Retrofitting of Existing Structures, Springer, 339p, 2018
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesassessed
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Customer Orientationassessed
Leadership and Responsibilityassessed
Self-presentation and Social Influence assessed
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsassessed
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
101-0159-00LMethod of Finite Elements II3 credits2GE. Chatzi, K. Tatsis
AbstractThe Method of Finite Elements II is a continuation of Method of Finite Elements I. Here, we explore the theoretical and numerical implementation concepts for the finite element analysis beyond the linear elastic behavior. This course aims to offer students with the skills to perform nonlinear FEM simulations using coding in Python.
*This course offers no introduction to commercial software.
ObjectiveThis class overviews advanced topics of the Method of Finite Elements, beyond linear elasticity. Such phenomena are particularly linked to excessive loading effects and energy dissipation mechanisms. Their understanding is necessary for reliably computing structural capacity.
In this course, instead of blindly using generic structural analysis software, we offer an explicit understanding of what goes on behind the curtains, by explaining the algorithms that are used in such software.

The course specifically covers the treatment of the following phenomena:
- Material Nonlinearity (Plasticity)
- Geometric Nonlinearity (Large Displacement Problems)
- Nonlinear Dynamics
- Fracture Mechanics
The concepts are introduced via theory, numerical examples, demonstrators and computer labs in Python (starting Fall 2021).

Upon completion of the course, the participants will be able to:
- Recognize when linear elastic analysis is insufficient
- Solve nonlinear dynamics problems, which form the core for limit state calculations (e.g. ultimate capacity, failure) of structures
- Numerically simulate fracture; a dominant failure phenomenon for structural systems.

See the class webpage for more information:
http://www.chatzi.ibk.ethz.ch/education/method-of-finite-elements-ii.html
Lecture notesThe course slides serve as Script. These are openly available on: http://www.chatzi.ibk.ethz.ch/education/method-of-finite-elements-ii.html
LiteratureCourse Slides (Script): http://www.chatzi.ibk.ethz.ch/education/method-of-finite-elements-ii.html

Useful (optional) Reading:
- Nonlinear Finite Elements of Continua and Structures, T. Belytschko, W.K. Liu, and B. Moran.
- Bathe, K.J., Finite Element Procedures, Prentice Hall, 1996.
- Crisfield, M.A., Remmers, J.J. and Verhoosel, C.V., 2012. Nonlinear finite element analysis of solids and structures. John Wiley & Sons.
- De Souza Neto, E.A., Peric, D. and Owen, D.R., 2011. Computational methods for plasticity: theory and applications. John Wiley & Sons.
Prerequisites / NoticePrerequisites:
-101-0158-01 Method of Finite Elements I (FS)
- A good knowledge of Python is necessary for attending this course.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Social CompetenciesCooperation and Teamworkassessed
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
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, O. Fink, 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.
101-0523-12LFrontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS21) Restricted registration - show details
Number of participants limited to 21.
1 credit2SM. A. Kraus, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. Lukovic, K. Schindler, B. Soja, B. Sudret, M. J. Van Strien
AbstractThis doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).
ObjectiveStudents will
• Critically read scientific papers on the recent developments in machine learning
• Put the research in context
• Present the contributions
• Discuss the validity of the scientific approach
• Evaluate the underlying assumptions
• Evaluate the transferability/adpatability of the proposed approaches to own research
• (Optionally) implement the proposed approaches.
ContentWith the increasing amount of data collected in various domains, the importance of data science in many disciplines, such as infrastructure monitoring and management, transportation, spatial planning, structural and environmental engineering, has been increasing. The field is constantly developing further with numerous advances, extensions and modifications.
The course aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).
Each student will select a paper that is relevant for his/her research and present its content in the seminar, putting it into context, analyzing the assumptions, the transferability and generalizability of the proposed approaches. The students will also link the research content of the selected paper to the own research, evaluating the potential of transferring or adapting it. If possible and applicable, the students will also implement the adapted algorithms The students will work in groups of three students, where each of the three students will be reading each other’s selected papers and providing feedback to each other.
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 in Structural Engineering0 credits1KW. Kaufmann, E. Chatzi, A. Frangi, B. Stojadinovic, B. Sudret, A. Taras, M. Vassiliou
AbstractProfessors from national and international universities, technical experts from the industry as well as research associates of the institute of structural engineering (IBK) are invited to present recent research results and specific projects from the practice. This colloquium is adressed to members of universities, practicing engineers and interested persons in general.
ObjectiveLearn about recent research results in structural engineering.