Suchergebnis: Katalogdaten im Herbstsemester 2023
Doktorat Bau, Umwelt und Geomatik  Mehr Informationen unter: https://www.ethz.ch/de/doktorat.html | |||||||||||||||||||||||||||
 Vertiefung FachwissenDen Doktorierenden D-BAUG steht (neben den unten aufgelisteten Kursen) das gesamte fachspezifische Lehrangebot der ETHZ und der Universität Zürich zur individuellen Auswahl offen, sofern es ein Angebot aus den speziell für Doktorierende konzipierten Lehrveranstaltungen oder regulären Lehrveranstaltungen des Master-Studiums oder des dritten Jahres des Bachelor-Studiums ist. | |||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 101-0191-00L | Seismic and Vibration Isolation | W | 2 KP | 1G | M. Vassiliou | ||||||||||||||||||||||
| Kurzbeschreibung | This course will cover the analysis and design of isolation systems to mitigate earthquakes and other forms of vibrations. The course will cover: 1. Conceptual basis of seismic isolation, seismic isolation types, mechanical characteristics of isolators. 2. Behavior and modeling of isolation devices, response of structures with isolation devices. 3. Design approaches and code requirements | ||||||||||||||||||||||||||
| Lernziel | After successfully completing this course the students will be able to: 1. Understand the mechanics of and design isolator bearings. 2. Understand the dynamics of and design an isolated structure. | ||||||||||||||||||||||||||
| Inhalt | 1. Introduction: Overview of seismic isolation; review of structural dynamics and earthquake engineering principles. Viscoelastic behavior. 2. Linear theory of seismic isolation 3. Types of seismic isolation devices - Modelling of seismic isolation devices – Nonlinear response analysis of seismically isolated structures in Matlab 4. Behavior of rubber isolators under shear and compression 5. Behavior of rubber isolators under bending 6. Buckling and stability of rubber isolators 7. Code provisions for seismically isolated buildings | ||||||||||||||||||||||||||
| Skript | The electronic copies of the learning material will be uploaded to ILIAS and available through myStudies. The learning material includes: reading material, and (optional) exercise problems and solutions. | ||||||||||||||||||||||||||
| Literatur | There is no single textbook for this course. However, most of the lectures are based on parts of the following books: • Dynamics of Structures, Theory and Applications to Earthquake Engineering, 4th edition, Anil Chopra, Prentice Hall, 2017 • Earthquake Resistant Design with Rubber, 2nd Edition, James M. Kelly, Springer, 1997 • Design of seismic isolated structures: from theory to practice, Farzad Naeim and James M. Kelly, John Wiley & Sons, 1999 • Mechanics of rubber bearings for seismic and vibration isolation, James M. Kelly and Dimitrios Konstantinidis, John Wiley & Sons, 2011 | ||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | 101-0157-01 Structural Dynamics and Vibration Problems course, or equivalent, or consent of the instructor. Students are expected to know basic modal analysis, elastic spectrum analysis and basic structural mechanics. | ||||||||||||||||||||||||||
| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  | W | 1 KP | 1S | V. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, M. J. Van Strien | ||||||||||||||||||||||
| 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. | ||||||||||||||||||||||||||
| 101-0139-00L | Scientific Machine and Deep Learning for Design and Construction in Civil Engineering | W | 3 KP | 4G | M. A. Kraus, D. Griego | ||||||||||||||||||||||
| Kurzbeschreibung | This course will present methods of scientific machine and deep learning (ML / DL) for applications in design and construction in civil engineering. After providing proper background on ML and the scientific ML (SciML) track, several applications of SciML together with their computational implementation during the design and construction process of the built environment are examined. | ||||||||||||||||||||||||||
| Lernziel | This course aims to provide graduate level introduction into Machine and especially scientific Machine Learning for applications in the design and construction phases of projects from civil engineering. Upon completion of the course, the students will be able to: 1. understand main ML background theory and methods 2. assess a problem and apply ML and DL in a computational framework accordingly 3. Incorporating scientific domain knowledge in the SciML process 4. Define, Plan, Conduct and Present a SciML project | ||||||||||||||||||||||||||
| Inhalt | The course will include theory and algorithms for SciML, programming assignments, as well as a final project assessment. The topics to be covered are: 1. Fundamentals of Machine and Deep Learning (ML / DL) 2. Incorporation of Domain Knowledge into ML and DL 3. ML training, validation and testing pipelines for academic and research projects A comprehensive series of computer/lab exercises and in-class demonstrations will take place, providing a "hands-on" feel for the course topics. | ||||||||||||||||||||||||||
| Skript | The course script is composed by lecture slides, which are available online and will be continuously updated throughout the duration of the course. | ||||||||||||||||||||||||||
| Literatur | Suggested Reading: Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong Mathematics for Machine Learning K. Murphy. Machine Learning: a Probabilistic Perspective. MIT Press 2012 C. Bishop. Pattern Recognition and Machine Learning. Springer, 2007 S. Guido, A. Müller: Introduction to machine learning with python. O'Reilly Media, 2016 O. Martin: Bayesian analysis with python. Packt Publishing Ltd, 2016 | ||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Familiarity with MATLAB and / or Python is advised. | ||||||||||||||||||||||||||
| 701-0015-00L | Transdisciplinary Research: Challenges of Interdisciplinarity and Stakeholder Engagement The lecture takes place if a minimum of 12 students register for it. | W | 2 KP | 1S | B. Vienni Baptista, C. E. Pohl, M. Stauffacher | ||||||||||||||||||||||
| Kurzbeschreibung | This seminar is designed for PhD students and PostDoc researchers involved in inter- or transdisciplinary research. It addresses and discusses challenges of this kind of research using scientific literature presenting case studies, concepts, theories, methods and by testing practical tools. It concludes with a 10-step approach to make participants' research projects more societally relevant. | ||||||||||||||||||||||||||
| Lernziel | Participants know specific challenges of inter- and transdisciplinary research and can address them by applying practical tools. They can tackle questions like: how to integrate knowledge from different disciplines, how to engage with societal actors, how to secure broader impact of research? They learn to critically reflect their own research project in its societal context and on their role as scientists. | ||||||||||||||||||||||||||
| Inhalt | The seminar covers the following topics: (1) Theories and concepts of inter- and transdisciplinary research (2) The specific challenges of inter- and transdisciplinary research (3) Collaborating between different disciplines (4) Engaging with stakeholders (5) 10 steps to make participants' research projects more societally relevant Throughout the whole course, scientific literature will be read and discussed as well as practical tools explored in class to address concrete challenges. | ||||||||||||||||||||||||||
| Literatur | Literature will be made available to the participants. The following open access article builds a core element of the course: Pohl, C., Krütli, P., & Stauffacher, M. (2017). Ten Reflective Steps for Rendering Research Societally Relevant. GAIA 26(1), 43-51 doi: 10.14512/gaia.26.1.10 available at (open access): Link Further, this collection of tools will be used https://naturalsciences.ch/topics/co-producing_knowledge https://www.shapeidtoolkit.eu | ||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Participation in the course requires participants to be working on their own research project. Dates (Wednesdays, 8h15-12h00): 27 September, 11 October, 25 October, 8 November, 22 November | ||||||||||||||||||||||||||
| Kompetenzen |
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| 101-0523-14L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering | W | 1 KP | 1G | V. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, 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. | ||||||||||||||||||||||||||
| Überfachliche Kompetenzen | |||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | ||||||||||||||||||||||
| » Lehrangebot in Erziehungswissenschaften für Lehrdiplom und DZ | |||||||||||||||||||||||||||
| » Sprachkurse ETH/UZH: siehe Wissenschaft im Kontext | |||||||||||||||||||||||||||
| 900-0100-DRL | Transferable Skills Course I (1-3 days) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 1 KP | 2S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. | ||||||||||||||||||||||||||
| 900-0101-DRL | Transferable Skills Course II (1-3 days) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 1 KP | 2S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. | ||||||||||||||||||||||||||
| 900-0102-DRL | Transferable Skills Course III (1-3 days) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 1 KP | 2S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. | ||||||||||||||||||||||||||
| 900-0103-DRL | Transferable Skills Course I (1-3 days, with Poster or Talk) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 2 KP | 4S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| 900-0104-DRL | Transferable Skills Course II (1-3 days, with Poster or Talk) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 2 KP | 4S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| 900-0105-DRL | Transferable Skills Course III (1-3 days, with Poster or Talk) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 2 KP | 4S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of short courses or workshops with a maximum duration of 3 days. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| 900-0106-DRL | Transferable Skills Course I (1 week) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 2 KP | 4S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. | ||||||||||||||||||||||||||
| 900-0107-DRL | Transferable Skills Course II (1 week) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 2 KP | 4S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. | ||||||||||||||||||||||||||
| 900-0108-DRL | Transferable Skills Course III (1 week) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 2 KP | 4S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. | ||||||||||||||||||||||||||
| 900-0109-DRL | Transferable Skills Course I (1 week, with Poster or Talk) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 3 KP | 6S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| 900-0110-DRL | Transferable Skills Course II (1 week, with Poster or Talk) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 3 KP | 6S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. Participants need to present either a poster or a talk at this occasion.. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| 900-0111-DRL | Transferable Skills Course III (1 week, with Poster or Talk) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 3 KP | 6S | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| Lernziel | Acquisition of transferable skills and cross-disciplinary competences in the range of courses or workshops with a minimum duration of 1 week. Participants need to present either a poster or a talk at this occasion. | ||||||||||||||||||||||||||
| 900-0112-DRL | Participation in Commission I (min 1 year) Only for doctoral students. Please select your doctoral thesis supervisor as a lecturer and prove your participation with the appropriate certificate. | W | 1 KP | 2P | Dozent/innen | ||||||||||||||||||||||
| Kurzbeschreibung | Active participation in commissions or university bodies, like associations of scientific staff, the university assembly or similar for at least 1 year. | ||||||||||||||||||||||||||
| Lernziel | Active participation in commissions or university bodies, like associations of scientific staff, the university assembly or similar for at least 1 year. | ||||||||||||||||||||||||||
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