Eleni Chatzi: Catalogue data in Spring Semester 2025 |
| 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 |
| chatzi@ibk.baug.ethz.ch | |
| URL | http://www.chatzi.ibk.ethz.ch/ |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0008-00L | Structural Identification and Health Monitoring  | 3 credits | 2G | E. Chatzi, V. Ntertimanis | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The 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 notes | The 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Suggested Reading: T. Söderström and P. Stoica: System Identification, Prentice Hall International: http://user.it.uu.se/~ts/sysidbook.pdf | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Familiarity with MATLAB is advised. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0114-10L | Theory of Structures II | 4 credits | 3V + 2U | E. Chatzi, A. Egger | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This 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). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Mastering 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Linear 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Simon Zweidler, "Baustatik II", 2017. Peter Marti, "Theory of Structures", Wiley, 2013, 679 pp. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Theory of Structures I | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0158-01L | Method of Finite Elements I | 5 credits | 3G | E. Chatzi, A. Egger | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Contents: – Introductory concepts In this introductory section, we discuss the background motivating adoption of finite element analysis and offer an overview of matrices and linear algebra. – The Direct Stiffness Method In this section, we overview the basic principles of the DSM method. We offer illustrative demos and exercises in Python. – Formulation of the Method of Finite Elements In this section, we overview the main ingredients to the formulation of the FE method, namely the Principle of Virtual Work; Isoparametric formulations. We discuss these formulations for both 1D Elements (truss, beam) and 2D Elements (plane stress/strain). We offer illustrative demos and exercises in Python. – Practical application of the Method of Finite Elements This section is concerned with use of the method into practice. We discuss practical considerations and move onto results interpretation onto realistic examples from actual use cases. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Structural 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 / Notice | Prior basic knowledge of Python is necessary. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0190-08L | Uncertainty Quantification and Data Analysis in Applied Sciences Does not take place this semester. 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 credits | 4G | E. Chatzi | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The topics to be covered are organized in three broad categories: 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: Dr. Stefano Marelli, Dr. Maliki Moustapha Track 2: Bayesian Inference and Uncertainty Propagation, offered the by the System Dynamics Laboratory, University of Thessaly, and the Chair of Structural Mechanics and Monitoring, ETH Zurich (18 hours) Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Antonios Kamariotis, Dr. Konstantinos Tatsis, Giacomo Arcieri Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics and Monitoring, ETH Zurich (18 hours) Lecturers: Dr. Vasilis Dertimanis, Prof. Dr. Eleni Chatzi The lectures will be complemented via a comprehensive series of interactive Tutorials. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Suggested 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 / Notice | Introductory course on probability theory Fair command on Matlab | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  | 1 credit | 1S | M. Lukovic, E. Chatzi, F. Corman, I. Hajnsek, V. Ntertimanis, K. Schindler, B. Soja, M. J. Van Strien | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0523-15L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering | 1 credit | 1G | M. Lukovic, E. Chatzi, F. Corman, I. Hajnsek, V. Ntertimanis, K. Schindler, B. Soja, M. J. Van Strien | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-1187-10L | Colloquium in Structural Engineering (Spring Semester) | 1 credit | 1K | A. Taras, E. Chatzi, A. Frangi, W. Kaufmann, B. Stojadinovic, B. Sudret | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Learn about recent research results in structural engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Learn about recent research results and novel practical applications & methods in structural engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||