Artificial Intelligence-based assessment of the resilience and seismic vulnerability of infrastructures at a network level
External reference persons MIANO ANDREA (Università di Napoli Federico II)
PARISI FULVIO (Università di Napoli Federico II)
Thesis type EXPERIMENTAL AND SIMULATION
Description The thesis work involves developing a methodology, based on data analysis and Machine Learning (ML), to evaluate the resilience of urban road networks in case of large seismic events. More specifically, the candidate student will begin by investigating one or more case studies of large road networks in highly-populated urban areas (Naples and Turin). This first part will mainly focus on the definition of fragility curves for all the major infrastructures (bridges and viaducts) included in the selected networks. This will provide estimates of the seismic vulnerability at the structural level for these most critical components. Then, departing from these evaluations and experimental data (seismic recordings and traffic analysis), a data-driven, AI-based approach will be developed in the second part of the thesis. This will be used to assess the global vulnerability of the whole road network, plus its resilience to traffic interruptions and re-routing.
This research work is part of the activities of the newly inaugurated Centro Nazionale Mobilità Sostenibile (CNMS), specifically of the Spoke 7 WP 4 ‘Resilience of networks, structural health monitoring and asset management’, led by Politecnico di Torino and that include a network of 11 top Italian universities, plus many industrial partners (including, but not limited to, Autostrade per l’Italia, Milano-Serravalle, ANAS, and more).
Deadline 08/06/2024 PROPONI LA TUA CANDIDATURA