The following student projects are currently available at EESD. If you are interested in any of the projects, please contact the person indicated in the project description. The exact scope of the project (e.g. semester or Master project) can be adjusted to your requirements.
We are developing a practical modeling framework for a wall constructed with demolition pieces. This is achieved using an efficient remeshing to represent the irregular microstructure obtained from point clouds as well as a new point cloud compression scheme. In this scheme, the point cloud data is saved as a set of expansion coefficients from the (hemi-) spherical (cap) and/or disk harmonics. There is no need for storing the surface data (vertices, lines, and faces) explicitly as we can retain such information from the reconstruction stage of the spectral method for any required resolution. Each individual demolition unit is stored as a coefficient corresponding to the most appropriate harmonic bases, which depends on the overall element topology.
Supervisors:
Hnat Lesiv, Katrin Beyer
This project aims to investigate the effect of bed joint reinforcement on the in-plane response of modern brick masonry under in-plane loading. The numerical model will follow a micro-modelling approach, where bricks are represented as continuum elements and mortar joints as discrete elements.
After calibrating the numerical model using available experimental data from tests conducted at EESD, the project will explore the impact of different bed-joint reinforcement typologies and ratios on the in-plane seismic response of brick masonry walls.
Through this project, you will deepen your knowledge in:
- Nonlinear analysis of structural elements
- Masonry mechanics
- Seismic design of masonry structures
Contact:
Ernesto Inzunza Araya ([email protected])
Savvas Saloustros ([email protected])
Katrin Beyer ([email protected])
This project aims to investigate the effect of flanges on the in-plane response of modern brick masonry under in-plane loading. The numerical model will follow a micro-modelling approach, where bricks are represented as continuum elements and mortar joints as discrete elements.
After calibrating the numerical model using available experimental data from tests conducted at EESD, the project will explore the impact of different geometries and configurations of flanges on the in-plane seismic response of brick masonry walls.
Through this project, you will deepen your knowledge in:
- Nonlinear analysis of structural elements
- Masonry mechanics
- Seismic design of masonry structures
Contact:
Ernesto Inzunza Araya ([email protected])
Savvas Saloustros ([email protected])
Katrin Beyer ([email protected])
Objective:
Apply an optimization-based rigid block modeling method to simulate pier spandrel systems* with macro elements.
(*Other case studies are also available)
Tasks:
- Identify possible failure mechanisms and discretize the structure with macro elements.
- Understand the optimization-based rigid block modeling method and learn how to use it from a Python interface.
- Limit analysis and pushover analysis of the case study.
Contact:
Qianqing Wang ([email protected])
Katrin Beyer ([email protected])
Objective:
Apply an optimization-based rigid block model to building-level analysis
Tasks:
- Understand the optimization-based rigid block modeling method and learn how to use it from the Python interface.
- Limit analysis and pushover analysis of the case study.
Contact:
Qianqing Wang ([email protected])
Katrin Beyer ([email protected])
Objective:
Analyze a rubble masonry building by combining microscale modeling and macroscale modeling methods.
Tasks:
- Understand optimization-based rigid block modeling method and learn how to use it from the Python interface.
- Identify different structural elements and their boundary conditions and loads.
- Create a micro model for each element and carry out the limit analysis.
- Create a macro model for each element based on results from the previous step.
- Assemble macro models of the full building and carry out pushover analysis of the building.
Contact:
Qianqing Wang ([email protected])
Katrin Beyer ([email protected])
Description:
Investigate the effect of spatially variable material properties of masonry, such as the elastic modulus, cohesion, friction coefficient, and damping, on the seismic response of unreinforced masonry (URM) buildings. By incorporating variability in these properties, the study aims to better capture the uncertainties in masonry and assess how they influence the behavior during earthquakes.
Numerical models using the equivalent frame model approach and probabilistic approach will be used to evaluate the dynamic response of an URM building.
Contact:
Mathias Haindl Carvallo ([email protected])
Katrin Beyer ([email protected])
Description:
Experimental campaigns have demonstrated an increase in effective stiffness with the applied axial load (Vanin et al., 2017; Vasconcelos, 2005; Bosiljkov et al., 2005). This study aims to evaluate the impact of different axial load ratio (ALR) levels on the structural response using the Equivalent Frame Model proposed by Vanin et al. (2020) in OpenSees. The analysis will be conducted through non linear static analysis (Pushover) considering a modulus of elasticity (E) that varies with the ALR, on walls of literature that already have experimental results.
The student will compare and analyze the results of the Pushover curves with the experimental curves, using the current constitutive law and a new one where E is a function of ALR.
Contact:
Johnatan Orjuela Mejia ([email protected])
Katrin Beyer ([email protected])
Description:
- Numerical modelling using open-source software „OpenSEES”
- Case studies in Zagreb, Croatia, affected by the 2020 Croatia Earthquakes
- Research question: How does a different height of the units influence behaviour, damage mechanisms, and fragility of units?
- Skills acquired:
- Dynamic analysis of nonlinear building models;
- Code-based hand calculations;
Contact:
Igor Tomić ([email protected])
Katrin Beyer ([email protected])
An experimental campaign on a new sustainable typology of masonry construction that enables reclaiming concrete demolition waste—using a technique reminiscent of traditional stone masonry with lime mortar- has identified the interface between mortar and demolition waste as the weakest part. How do various sustainable mortar types and classes impact the structural response of concrete demolition waste masonry walls?
Objective:
Evaluation and analysis of the mechanical properties and life cycle assessment (LCA) of masonry walls incorporating concrete demolition waste, based on experimental campaign at the interface level.
Tasks:
- Testing of the updated setup for interface experiments.
- Life Cycle Analysis (LCA) of different masonry solutions.
- Experimental campaign on the mortar-concrete interface, considering various mortar types (M5–M15, OPC, and LC3) and different levels of precompression load.
- Post-processing and analysis of experimental results.
- Numerical modelling and simulation at the wall level, integrating findings from the experimental campaign.
Contact:
Jakov Oreb ([email protected])
Igor Tomić ([email protected])
Katrin Beyer ([email protected])
Description:
Digital twinning of structures such as stone masonry walls demands robust and accurate 3D registration. The quality of the digital twin depends on how well key features can be extracted and matched from images. Traditional methods like SIFT, SURF, and ORB have been used for years, but they often struggle in difficult conditions such as low texture, extreme viewpoint changes, or varying lighting. Deep learning approaches like SuperPoint and D2-Net offer a more advanced solution by learning key point detection and description end-to-end, making them more reliable and precise.
In this project, we will implement and compare these methods for 3D reconstruction, evaluating their accuracy, robustness, and efficiency. The goal is to improve the fidelity of digital twins for applications in structural analysis, heritage conservation, and automated construction.
Tasks:
- Implement and compare traditional (SIFT, ORB) and deep learning (SuperPoint, D2-Net) feature matching methods.
- Integrate both methods into a 3D reconstruction and digital twinning pipeline.
- Evaluate performance based on accuracy, robustness, and computational efficiency.
Contact:
Mati Ullah Shah ([email protected])
Savvas Saloustros ([email protected])
Katrin Beyer ([email protected])
