Master projects

This Master’s project focuses on the advanced seismic analysis of existing buildings, with particular attention to the role of soil-structure interaction (SSI). Although often neglected in standard assessment procedures, SSI can have a decisive impact in specific applications—especially in soft soil conditions or for flexible structures. The project aims to identify these critical cases and develop more accurate modeling approaches that integrate SSI effects. Through a combination of literature review, numerical modeling, and a targeted case study, this work seeks to clarify when and how SSI should be accounted for to improve the reliability of seismic vulnerability assessments and inform appropriate retrofit strategies.

Deep geological repositories are the internationally preferred solution for the long-term disposal of high-level radioactive waste. Ensuring the integrity and safety of these systems over thousands of years requires robust monitoring of repository-induced effects—particularly temperature, pore water pressure, displacement, and stress—within the engineered and geological barriers.

This MSc thesis focuses on developing advanced, data-driven forecasting models using graph neural networks (GNNs) to predict the evolution of key parameters—temperature, pore water pressure, displacement, and stress—based on long-term, spatially distributed sensor data.

The available data is sourced from heterogeneous sensor modalities, including:

• Fibre optical cables (distributed temperature or strain sensing)
• Thermometers and humidity sensors
• Thermal conductivity probes
• Pressure cells
• Extensometers
• Gas sampling ports and gas sensors
• Corrosion coupons
• Seismic sources and receivers
• Geophysical monitoring pipes (GPx)

Key objectives include:

• Construct a graph-based representation of the repository nearfield, encoding spatial topology, sensor locations, and interdependencies.
• Integrate multi-modal time-series data into a unified framework for learning spatiotemporal correlations.
• Develop and benchmark GNN-based predictive models (e.g., spatiotemporal GCNs, graph attention networks, temporal GNNs) for forecasting THM evolution.
• Compare performance to classical time-series methods
• Apply uncertainty quantification techniques using ensemble learning or Bayesian GNNs to assess reliability.

This work bridges the gap between experimental monitoring and operational forecasting, providing a critical tool for ensuring the long-term safety and reliability of nuclear waste disposal systems.

Supervision: Prof. Lyesse Laloui [email protected] (LMS lab) & Prof. Olga Fink [email protected] (IMOS Lab)

• Design and analysis of thermal piles in building foundations
• Assessment of energy potential in tunnels

More information is available at: https://geoeg.net/
Contact: Dr. Elena Ravera ( [email protected] )

Tasks

• Novel methodology development: Formulate a new theoretical approach for applying loads on retaining walls considering soil elasto-plasticity.
• Script development for loading scenarios: Develop scripts tailored for each loading scenario, enhancing the versatility of the analysis.
• Elasto-plastic methodology design: Devise a unique elasto-plastic methodology to supplement the theoretical approach.
• Calculation tool creation: Build a comprehensive tool that integrates the novel methodology for efficient and accurate analysis of retaining walls under external loads.

Documentation

Contact: Angelica Tuttolomondo ( [email protected] ), John Eichenberger ( [email protected] )

• Students are also encouraged to propose their own projects in the context of the laboratory activities.

More information is available at: https://www.epfl.ch/labs/lms/
Contact: Prof. Lyesse Laloui ( [email protected] )