| KEYWORD |
Structural Health Monitoring for damage detection with AI approaches
Thesis abroad
keywords STRUCTURAL HEALTH MONITORING
Reference persons GIUSEPPE CARLO MARANO
External reference persons PhD Candidate Raffaele Cucuzza, PhD Candidate Marco Martino Rosso
Thesis type THEORETICAL AND SIMULATION
Description Structural Health Monitoring deals with model updating tracking modal parameters changes over time in order to define warning and emergency alert systems. The two main methods can be regrouped in model-based approaches or data-driven strategies. In these latter, continuous monitoring produces big data which can be effectively dealt with Machine Learning and Artificial Intelligence algorithms in order to obtain a dimensionality reduction toward the fundamental damage sensitive features. In this Thesis, the candidate must learn to deal with SHM issues and implement ML and AI algorithms to produce innovative monitoring systems for new and existing structures and infrastructures. This Thesis aims also to provide to the candidate all the fundamental competencies regarding the structural health monitoring of large structures, such as bridges, with less invasive techniques which can also be adopted in operational conditions in order to ensure safety assessments, define a programmed maintenance plan, etc. to provide all the main skills nowadays high demanded in Structural Civil Engineering field.
Required skills Matlab, Python principles
Notes To learn during the Thesis: Matlab or Python programming and Machine Learning algorithms
Deadline 15/12/2021
PROPONI LA TUA CANDIDATURA