Master Theses

Simulating Structural Dynamics with Graph Neural Networks

Supervisors: Gregory Duthé (), Kostas Vlachas (), Prof. Dr. Eleni Chatzi

Content
Graph Neural Networks (GNNs) have recently emerged as a powerful way to learn the dynamics of a physical process. Using standard meshing techniques, it is easy to decompose a physical problem, such as the dynamical simulation of a structure into a discrete graph, which a GNN can then operate on. In this thesis, we investigate the use of GNNs as alternative structural dynamics simulators. We will explore operations on graphs (incl. message passing) and their equivalence to sub-structuring and condensation schemes. It will also be important to include as many physics-based inductive biases as possible.

The student(s) will have the following objectives:

• Review the existing methods and literature
• Become familiar with the PyTorch Geometric framework
• Implement a first prototype, based on existing work from the literature
• Formulate methods for sub-structuring/condensation schemes which exploit the expressivity of GNNs
• Demonstrate the method on a simple case study
  

Suggested Courses: 101-0157-01L Structural Dynamics and Vibration Problems

Suggested Competencies: Python, PyTorch (or equivalent ML framework)

Exploring Applications of Physical Computing to
Hybrid Modeling for Twinning and StructuralCondition and Structural Health Monitoring

Supervisors: Dr. Marc Serra Garcia (AMOLF), Dr. Bart Van Damme (Empa), Dr. Andrea Bergamini (Empa),Prof. Dr. Eleni Chatzi (ETH)

Content

Advances in numerical
modeling, of linear and non-linear mechanical metamaterials, as well as machine
learning, are leading to an exciting new area of mechanics: Physical signal
processing. The fundamental principle in physical signal processing consists in
using the natural dynamical response of a Micro-Electromechanical System (MEMS)—for example, its resonance characteristics, to
detect specific acoustic signatures with near-zero power. As an example, recent
work has demonstrated the ability of a MEMS device to discriminate between
different spoken words presented to it, and correctly categorize them. The
implications of this capability are wide-ranging; from the development of
passive voice-activated devices that activate with specific keywords, guaranteeing the privacy of users, to the classification of complex acousticsignals based on specific fingerprint features.

This master thesis project, will build a bridge between physical signal processing and condition monitoring of composite structures by establishing the functional relationship between acoustic emission signals from damage event in composites and classification capabilities of MEMS physical computing devices.  The ultimate goal of this effort will be the fabrication of very low cost devices that can be distributed on structures as a ‘smart dust’ and will provide information about their state, if remotely interrogated.

Expected project outcomes

1. Literature survey of acoustic signals from typical damage events in composite structures, both in laboratory scale and real-life structures. 
2. Familiarize with and analyze actual acoustic emission data, as found for example on https://data.4tu.nl OR https://data.mendeley.com/datasets/svz439j8hb/1 to determine fundamental quantities such as duration, power spectral density to identify basic design features of such signals. 
3. Set up the environment for the in-silico training and validation of a mechanical device for the identification of available events.

If time permits:

1. Collect signals from tests on laboratory scale samples under controlled conditions representative of actual damage events and non-significant events (such as Hsu-Nielsen test) and pre-process them.

1. Train the network on collected data.

Required Competencies

Structural mechanics/dynamics, Finite Element Modelling, Computer programming experience will be beneficial

Software Knowledge

• Proficient in Python (or MATLAB) coding

Digital Twins and Temperature-Aware Monitoring for Bridge Performance Assessment

Supervisors: Dr. Yves Reuland (irmos technologies AG), Dr. Minas Spyridonakos (irmos technologies AG), Prof. Dr. Eleni Chatzi (ETH Zürich)

Content
Bridges are critical components of modern transport networks and must remain safe and functional well beyond their original design life. Increasing traffic loads, environmental variability, and aging materials challenge traditional assessment approaches based on periodic inspections and conservative safety margins.

Continuous monitoring combined with physics-based and data-driven modeling enables the development of digital twins — dynamic representations of structural behavior under real operating conditions. Such frameworks support safety assessment, help define thresholds for normal behavior, and improve early damage detection.

A central challenge lies in understanding the interaction between operational loads (traffic, wind) and environmental influences, particularly temperature, which induces spatially and temporally varying structural responses. This thesis offers two complementary research directions within this framework.

Possible Direction A: Data-Driven Digital Twin Development

Focus: Understanding how operational and environmental conditions influence structural dynamics and embedding this knowledge into predictive models.

Main tasks:

• Analyze real monitoring data to quantify the influence of traffic and environmental conditions on modal properties and structural response.
• Calibrate and validate a physics-based bridge model using measured data.
• Develop a reduced or surrogate model to predict structural behavior under varying operational scenarios.
• Define data-informed indicators and thresholds for SHM applications.

Possible Direction B: Temperature-Induced Structural Dynamics

Focus: Explicit modeling of temperature effects and their impact on monitoring interpretation

Main tasks:

• Develop a Finite Element model incorporating temperature-dependent material behavior.
• Simulate spatially varying temperature fields and evaluate their effect on modal characteristics.
• Compare simulated temperature-induced dynamics with monitoring data.
• Assess how temperature variations may mask or mimic damage-related signatures.
  

Suggested Courses: Structural Identification and Health Monitoring, course on machine learning methods

Suggested Competencies: Python/MATLAB

Improving the design of rotating parts of wind turbines through metamaterial technology

Supervisors: Dr. Vasilis Dertimanis (), Prof. Dr. Eleni Chatzi

Industry Partners: Dr. Luca Sangiuliano, Phononic Vibes

Content

In the context of novel lightweight materials for noise and vibration reduction, Phononic Vibes Srl (https://phononicvibes.com/) is researching, designing and producing metamaterial technology. Metamaterials are often periodic structures, consisting of a host structure with embedded or added resonant structures, leading to superior vibro-acoustic attenuation performance in some tunable frequency ranges, called stop bands. In the following videos, the metamaterial concept is applied structure-borne (https://www.youtube.com/watch?v=DUee93HcPVQ&t) and airborne noise problems.

The resonant structures often have some small features that control their tuned frequency and the dynamic behavior of the entire metamaterial system. Once these resonant structures are applied to rotating systems, the stop band behaviour can be modulated according to the rotation speed to the entire system thanks to the presence of inertial forces (ref. 1). This interesting phenomenon can give rise to new methods to control the dynamic behavior of rotating systems and to reduce the unwanted dynamics.

Rotating parts of wind-turbines are often subjected to extreme dynamic conditions. To ensure the long service life and optimal design of rotating parts of wind-turbines, thus preventing failures and expensive maintenances, the dynamic behaviour of these parts must be thoroughly designed and controlled.

The goal of this master thesis is then to investigate the potential of metamaterial technology applied to the rotating parts of wind turbines to control and preserve their dynamic behaviour. In particular, the student will explore how to exploit the modulation effect given by the inertial forces to tune the metamaterial system to reduce critical dynamic conditions. Analytical and numerical models will be developed by the student to study the physics and to assess the metamaterial performance applied to such systems. The aim is to propose design guidelines to achieve a robust design solution. 

This thesis is in collaboration with Phononic Vibes Srl and is mainly simulation-based.

Suggested Competencies: Python/MATLAB

Advanced Data-Driven Methods for Infrastructure Monitoring and Maintenance Optimization

Supervisors: Dr. Charikleia Stoura, Prof. Dr. Eleni Chatzi 

Industry Supervisor: Dr. Giulia Aguzzi, KISTLER Instrumente AG

Content

Effective monitoring of civil infrastructure is vital for ensuring long-term safety, serviceability, and cost-efficiency. In recent years, the proliferation of multi-sensor systems and the availability of high-fidelity vibration, acceleration, and Weigh-In-Motion (WIM) data have opened new avenues for developing intelligent monitoring frameworks. These advancements present an opportunity to transition from periodic inspections to predictive maintenance regimes, enhancing asset management strategies in transport infrastructure.

This Master’s thesis aims to develop and benchmark modern AI-assisted methodologies for the analysis of real-world monitoring datasets from bridges and rail infrastructure, with data access supported by KISTLER. The work will focus on synthesizing information from various sources, including vibration signals, tilt measurements, and WIM data, to derive actionable insights into the condition and behavior of structures under operational loading.

The scope includes:

• Developing pipelines for data fusion and pre-processing of multi-sensor time series.
• Performing comparative analyses of existing Structural Health Monitoring (SHM) methods and fault detection strategies.
• Evaluating the added value of WIM data in augmenting structural response interpretation and load estimation.
• Exploring machine learning and signal decomposition techniques (e.g., modal analysis, dictionary learning, supervised/unsupervised ML) for enhanced pattern recognition.
• Investigating tilt estimation through acceleration measurements, with potential use in low-cost sensor deployment scenarios.
• Applying insights to optimize rail surface monitoring and recommend maintenance strategies.
  

This interdisciplinary thesis connects experimental data analysis, system identification, and data-driven modeling with strong industrial relevance. Students will have the opportunity to work with cutting-edge sensing technologies and contribute toward the digital transformation of infrastructure maintenance.

Hybrid Modeling for Twinning and Structural
Health Monitoring in Engineered Systems

Supervisors: Dr. Konstantinos Vlachas (), Prof. Dr. Eleni Chatzi 

Content

Ensuring the safe, efficient, and resilient operation of structural assets throughout their life cycle requires the integration of sensing data with computational models that are both rapid to evaluate and reliable. This hybrid paradigm is particularly critical for systems subject to uncertainty, evolving conditions, or extreme events that may push their response beyond design assumptions. This thesis will focus on developing and validating hybrid representations that combine physics-based and data-driven models for structural response prediction, uncertainty quantification, and real-time model adaptation. The aim is to demonstrate the potential of such hybrid formulations in advancing digital twin frameworks for the optimized operation and maintenance of real-life structures. Depending on their background and interests, students may work on one or more of the following tasks:

Expected project outcomes

1. Development of reduced-order but reliable models for dynamic response prediction
2. Integration of sensing and vibration data with physics-based models
3. Implementation of uncertainty quantification and adaptive updating strategies
4. Real-time data assimilation and model adaptation under varying operating conditions
5. Experimental validation of hybrid digital twin approaches

Required Competencies

Structural mechanics/dynamics, Finite Element Modelling
Computer programming experience will be beneficial

Software Knowledge

• Proficient in Python (or MATLAB) coding