Vasileios Ntertimanis: Catalogue data in Autumn Semester 2022 |
| Name | Dr. Vasileios Ntertimanis |
| Address | Strukturmechanik und Monitoring ETH Zürich, HIL E 33.2 Stefano-Franscini-Platz 5 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 79 45 |
| v.derti@ibk.baug.ethz.ch | |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 101-0157-01L | Structural Dynamics and Vibration Problems | 3 credits | 2G | M. Vassiliou, V. Ntertimanis | |
| Abstract | Fundamentals of structural dynamics are presented. Computing the response of elastic single and multiple DOF structural systems subjected to harmonic, periodic, pulse, and impulse is discussed. Practical solutions to vibration problems in flexible structures under diverse excitations are developed. | ||||
| Learning objective | After successful completion of this course the students will be able to: 1. Explain the dynamic equilibrium of structures under dynamic loading. 2. Use second-order differential equations to theoretically and numerically model the dynamic equilibrium of structural systems. 3. Model structural systems using single-degree-of-freedom and multiple-degree-of-freedom models. 4. Compute the dynamic response of structural system to harmonic, periodic, pulse, and impulse excitation using time-history and response-spectrum methods. 5. Use dynamics of structures to identify the basis for structural design code provisions related to dynamic loading. | ||||
| Content | This is a course on structural dynamics, an extension of structural analysis for loads that induce significant inertial forces and vibratory response of structures. Dynamic responses of elastic and inelastic single-degree-of-freedom and multiple-degree-of-freedom structural systems subjected to harmonic, periodic, pulse, and impulse excitation are discussed. Theoretical background and engineering guidelines for practical solutions to vibration problems in flexible structures caused by humans, machinery, wind or explosions are presented. | ||||
| Lecture notes | The class will be taught mainly on the blackboard. Accompanying electronic material will be uploaded to ILIAS and available through myStudies. All the material can be found in Anil Chopra's comprehensive textbook given in the literature below. | ||||
| Literature | Dynamics of Structures: Theory and Applications to Earthquake Engineering, 4th edition, Anil Chopra, Prentice Hall, 2014 (Global Edition), ISBN-10: 9780273774242 Vibration Problems in Structures: Practical Guidelines, Hugo Bachmann et al., Birkhäuser, Basel, 1995 Weber B., Tragwerksdynamik. http://e-collection.ethbib.ethz.ch/cgi-bin/show.pl?type=lehr&nr=76 .ETH Zürich, 2002. | ||||
| Prerequisites / Notice | Knowledge of the fundamentals in structural analysis, and in structural design of reinforced concrete, steel and/or wood structures is mandatory. Working knowledge of matrix algebra and ordinary differential equations is required. Familiarity with Matlab and with structural analysis computer software is desirable. | ||||
| 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. | ||||
| 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. | ||||
| 101-0523-13L | 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). | ||||
| 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. | ||||
| 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. | ||||
| Learning 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. | ||||