Alessio Crocetti
Machine Learning approaches for damage detection strategy in Structural Health Monitoring: application to experimental data.
Rel. Rosario Ceravolo, Gaetano Miraglia, Giorgia Coletta, Linda Scussolini. Politecnico di Torino, Master of science program in Civil Engineering, 2023
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (9MB) | Preview |
Abstract
Structural Health Monitoring (SHM) is a discipline born during the last decades of the 20th century with the aim of implementing damage detection strategies in many engineering fields. These processes rely on structural observations that can be carried out through periodical dynamic response measurements coming from sensors installed on the structures. Datasets regarding a broad range of the lifetime of the structure are often required in order to implement efficient monitoring systems. Furthermore, analyses on the data should be fine evaluated in order to integrate and discern from the periodic or continuous measurements the noise (environmental) ones that may otherwise affect the results in a strong way.
The first approach towards SHM is constituted by the model-driven methods: they can be developed starting from an updating of a Finite Element Model (FEM) by means of an inverse approach
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modify record (reserved for operators) |
