Eleni Chatzi: Katalogdaten im Herbstsemester 2021 |
| Name | Frau Prof. Dr. Eleni Chatzi |
| Lehrgebiet | Strukturmechanik 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 |
| chatzi@ibk.baug.ethz.ch | |
| URL | http://www.chatzi.ibk.ethz.ch/ |
| Departement | Bau, Umwelt und Geomatik |
| Beziehung | Ordentliche Professorin |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0129-00L | Non Destructive Evaluation & Rehabilitation of Existing Structures | 3 KP | 2G | E. Chatzi, B. Herraiz Gómez, G. Kocur | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to non destructive evaluation tools and quantitative structural analyses and verifications for condition assessment of existing structures and subsequent decisions on their rehabilitation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | J. 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kompetenzen |
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| 101-0159-00L | Method of Finite Elements II | 3 KP | 2G | E. Chatzi, K. Tatsis | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | The course slides serve as Script. These are openly available on: http://www.chatzi.ibk.ethz.ch/education/method-of-finite-elements-ii.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Course 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: -101-0158-01 Method of Finite Elements I (FS) - A good knowledge of Python is necessary for attending this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  Findet dieses Semester nicht statt. Number of participants limited to 21. | 1 KP | 2S | B. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, K. Schindler | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Current 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Current research at D-BAUG will be presented and discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0523-12L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS21) Number of participants limited to 21. | 1 KP | 2S | M. A. Kraus, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. Lukovic, K. Schindler, B. Soja, B. Sudret, M. J. Van Strien | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This 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). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | With 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-1187-00L | Kolloquium Baustatik und Konstruktion | 0 KP | 1K | W. Kaufmann, E. Chatzi, A. Frangi, B. Stojadinovic, B. Sudret, A. Taras, M. Vassiliou | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Das 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 Hochschulangehörige, als auch an Ingenieure aus der Praxis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Neue Forschungsergebnisse aus dem Fachbereich Baustatik und Konstruktion kennen lernen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||