Vasileios Ntertimanis: Catalogue data in Spring Semester 2018
|Name||Dr. Vasileios Ntertimanis|
Strukturmechanik und Monitoring
ETH Zürich, HIL E 33.2
|Telephone||+41 44 633 79 45|
|Department||Civil, Environmental and Geomatic Engineering|
|101-0008-00L||Identification Methods for Structural Systems |
Does not take place this semester.
|3 credits||2G||E. Chatzi, V. Ntertimanis|
|Abstract||This course will present methodologies for defining a structural system, and assessing its condition based on structural response data. This data is made available via measurements, which are nowadays available from low-cost and easily deployed sensor technologies. The course will explain how engineers may exploit technology for designing and maintaining a safe and resilient infrastructure.|
|Objective||This course aims at providing a graduate level introduction into the modeling and identification of structural systems. The goal is to establish relationships governing the system behavior and to identify the characteristics (mechanical, geometrical properties) of the system itself, based on noisy or incomplete measurements of the structural response.|
The course will include theory, as well as laboratory and actual-scale structural testing, thereby offering a well-rounded overview of the ways in which we may extract response data from structures.
|Content||The topics to be covered are :|
- Fundamentals of vibrational analysis, signal processing and structural system representation
- Modal Testing, Operational Modal Analysis
- Parametric & Nonparametric Identification: Frequency Domain decomposition, Least Squares methods, ARMA models, Bayesian approaches.
- Heuristic methods: Genetic Algorithms, Neural Networks.
The differences between linear and nonlinear system identification will also be addressed.
A comprehensive series of computer/lab exercises and in-class demonstrations will take place, providing a "hands-on" feel for the course topics.
The final grade will be obtained, either
- by 30% from the graded exercises and 70% from the written session examination, or
- by the written session examination exclusively.
The highest ranking of the above two options will be used, so that assignments are only used to strengthen the grade.
|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: http://www.chatzi.ibk.ethz.ch/education/identification-methods-for-structural-systems.html|
T. Söderström and P. Stoica: System Identification, Prentice Hall International: http://user.it.uu.se/~ts/sysidbook.pdf
|101-0190-08L||Uncertainty Quantification and Data Analysis in Applied Sciences |
The course should be 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, P. Chatzidoukas, P. Koumoutsakos, S. Marelli, V. Ntertimanis, K. Papadimitriou, B. Sudret|
|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.|
|Objective||The course is offered as part of the Computational Science Zurich (CSZ) (http://www.zhcs.ch/) graduate program, a joint initiative between ETH Zürich and University of Zürich. This CSZ 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 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 (20 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 (20 hours)
Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Panagiotis Hadjidoukas, Prof. Dr. Petros Koumoutsakos
Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics, ETH Zurich (20 hours)
Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis.
The lectures will be complemented via a comprehensive series of interactive Tutorials will take place.
|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.|
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