Candidate ID: DC4
Topic: Optimal predictive infrastructure maintenance planning based on explainable AI for geotechnical systems
Beneficiary: TUM
My name is Lorenzo Brocchi and I’m from Italy. I recently graduated from Politecnico di Torino within the Smart Infrastructures, which integrates civil engineering with statistical modelling, AI and IoT technologies. In my Master’s thesis, I worked on damage identification in bridge girders using clustering techniques applied to multidimensional datasets combining acoustic emission, dynamic and static monitoring data, with the goal of supporting technicians through AI-based decision tools.
I joined the FOURIER project as Doctoral Candidate 4 at the Engineering Risk Analysis Group of the Technical University of Munich. My PhD project, Optimal predictive maintenance planning of geotechnical systems based on explainable AI, develops interpretable AI-driven strategies for infrastructure maintenance, combining heuristic and theory-guided machine learning to enhance the safety, sustainability and resilience of geotechnical systems.
I chose FOURIER because its interdisciplinary network and strong link between research and real-world applications provide the ideal environment to advance innovative approaches to risk analysis and resilient infrastructure management.
