Vasileios Ntertimanis: Catalogue data in Spring Semester 2025

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
E-mail	v.derti@ibk.baug.ethz.ch
Department	Civil, Environmental and Geomatic Engineering
Relationship	Lecturer

Number	Title	ECTS	Hours	Lecturers
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.
101-0522-10L	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.