Thesis defence by Hugo GABRIELIDIS

High-performance machine learning and data analytics for next-generation railway design

The MINERVE project aims to create a digital twin of the French railway infrastructure,  which will then be used to assess the state of the railway and predict its evolution. To achieve such goal, the project requires a robust architecture capable of collecting, storing, and analyzing large amounts of data from various sources. During my thesis, we developed a theoretical big data architecture tailored to MINERVE's objectives, based on the Zaloni zone data lake architecture, designed to be implemented on the Paris-Saclay mesocenter, a high-performance computing facility. We then used this theoretical architecture to implement two use-cases that would support the development of MINERVE Digital Twins. The first use case is the development of a pipeline for the semantic segmentation of large railway point clouds, a needed work to enhance the automation of railway infrastructure monitoring and maintenance. The second use case focuses on the generation of high-frequency seismic waves from low-frequency ones, a necessary step in the making of an accurate Digital Twin, as the ability to detect earthquakes and other seismic events is crucial for the safety and reliability of railway operations.

Composition du jury :

•          Mme Lydia Boudjeloud-Assala, Professeure de universités, Laboratoire LORIA - Université de Lorraine, Rapporteur.
•          M. Francisco Chinesta, Professeur des universités, Laboratoire PIMM - Arts et Métiers, Rapporteur.
•          M. Régis Cottereau, Chargé de recherche, Laboratoire de Mécanique et d’Acoustique - Centrale Marseille, Examinateur.
•          M. Stéphane Genaud, Professeur, Université de Strasbourg, Examinateur.
•          Mme Wassila Ouerdane, Professeur des universités, CentraleSupélec, Examinatrice.