Eleni Chatzi: Catalogue data in Autumn Semester 2022 |
| Name | Prof. Dr. Eleni Chatzi |
| Field | Structural 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 |
| chatzi@ibk.baug.ethz.ch | |
| URL | http://www.chatzi.ibk.ethz.ch/ |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Associate Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  Number of participants limited to 21. | 1 credit | 1S | M. J. Van Strien, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, V. Ntertimanis, K. Schindler, B. Soja | |
| Abstract | 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. | ||||
| 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 | ||||
| Content | Current research at D-BAUG will be presented and discussed. | ||||
| Prerequisites / Notice | 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. | ||||
| 101-0523-13L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS22) | 1 credit | 1G | M. J. Van Strien, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, V. Ntertimanis, K. Schindler, B. Soja | |
| Abstract | 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). | ||||
| Objective | 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. | ||||
| Content | 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. | ||||
| Prerequisites / Notice | 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 | Colloquium in Structural Engineering | 0 credits | 2K | A. Taras, E. Chatzi, A. Frangi, W. Kaufmann, B. Stojadinovic, B. Sudret, M. Vassiliou | |
| Abstract | Professors 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. | ||||
| Objective | Learn about recent research results in structural engineering. | ||||
| 173-0007-00L | Dynamics Only for MAS in Advanced Fundamentals of Mechatronics Engineering | 5 credits | 11G | E. Chatzi, V. Ntertimanis, P. Tiso | |
| Abstract | The course offers an introduction to dynamics of engineering systems. The first part focuses on Newtonian dynamics and energy principle to systems of particles and rigid bodies. The second part focuses on the free and forced response of single- and multi-degrees-of-freedom linear systems. Hands-on exercises, computer-based labs and experimental demos will support the theoretical lectures. | ||||
| Objective | After successful completion of this course the students will be able to: 1. Set up the kinematic description of a system of particles and rigid bodies subject to constraints. 2. Formulate the governing equations of motion of a system particles or of rigid bodies using balance law. 3. Alternative from the above, the student will be able to derive the equations of motion using Lagrange’s equations, d’Alembert’s principle, and Hamilton’s principle. 4. Find the equilibrium configurations of a given system, and perform linearization. 5. Compute the dynamic response of discrete systems to harmonic, periodic, pulse, and impulse excitation using time-history and response-spectrum methods. | ||||
| Content | Day-by-day course content: Week 1 Day 1 – Recap on Newtonian Dynamics for single particle Day 2 – Kinetics of systems of particles Day 3 – Kinetics of Rigid bodies Day 4 – Analytical mechanics Week 2 Day 6 – Mechanical Vibrations Day 7 – Elements of Structural Vibration - SDOF Day 8 – Elements of Vibration Theory - MDOF Day 9 – State Space Representations Day 10 – Transformations | ||||
| Lecture notes | The material will be organized in lecture slides. | ||||
| Literature | A specific list of books will be offered as useful/supplemental reading. | ||||