Search result: Catalogue data in Autumn Semester 2023

Doctorate Civil, Environmental and Geomatic Engineering Information
More Information at: https://www.ethz.ch/en/doctorate.html
Subject Specialisation
In addition to the courses listed below, D-BAUG doctoral students are free to choose from the entire range of subject-specific courses offered by ETHZ and the University of Zurich, provided that it is an offering specifically designed for doctoral students or a course of the regular Master’s program or of the third year Bachelor’s program.
NumberTitleTypeECTSHoursLecturers
101-0191-00LSeismic and Vibration IsolationW2 credits1GM. Vassiliou
AbstractThis 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
Learning objectiveAfter 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.
Content1. 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
Lecture notesThe 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.
LiteratureThere 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
Prerequisites / Notice101-0157-01 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.
101-0522-10LDoctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering Restricted registration - show details W1 credit1SV. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, M. J. Van Strien
AbstractCurrent 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
ContentCurrent research at D-BAUG will be presented and discussed.
Prerequisites / NoticeThis 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.
101-0139-00LScientific Machine and Deep Learning for Design and Construction in Civil Engineering Restricted registration - show details W3 credits4GM. A. Kraus, D. Griego
AbstractThis 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.
Learning objectiveThis 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
ContentThe 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.
Lecture notesThe course script is composed by lecture slides, which are available online and will be continuously updated throughout the duration of the course.
LiteratureSuggested 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
Prerequisites / NoticeFamiliarity with MATLAB and / or Python is advised.
701-0015-00LTransdisciplinary Research: Challenges of Interdisciplinarity and Stakeholder Engagement
The lecture takes place if a minimum of 12 students register for it.
W2 credits1SB. Vienni Baptista, C. E. Pohl, M. Stauffacher
AbstractThis 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.
Learning objectiveParticipants 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.
ContentThe 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.
LiteratureLiterature 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
Prerequisites / NoticeParticipation 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
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesfostered
Method-specific CompetenciesAnalytical Competenciesfostered
Problem-solvingfostered
Social CompetenciesCooperation and Teamworkfostered
Sensitivity to Diversityfostered
Personal CompetenciesCritical Thinkingfostered
Self-awareness and Self-reflection fostered
101-0523-14LFrontiers in Machine Learning Applied to Civil, Env. and Geospatial EngineeringW1 credit1GV. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, M. J. Van Strien
AbstractThis 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 objectiveStudents 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.
ContentWith 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 / NoticeThis 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.
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