Davide Raviolo
Om
PhD Project
My research focuses on advancing Structural Health Monitoring (SHM) techniques for detecting structural damage by gathering data from representative road bridges in the operational environment. The primary research interests are:
- Automated data processing methods for SHM through machine learning algorithms that analyze sensor data and extract relevant features.
- Methodologies for updating finite element models of bridges based on sensor data for effective damage detection.
- Robust and automatic procedures for detecting changes in the bridge structure that may impact its safety.
- Study relationships between environmental factors (e.g., temperature, humidity, wind) and the bridge's dynamic response.
By addressing these topics, the research aims to contribute to the development of more robust and reliable SHM techniques. The developed techniques can facilitate early detection of structural damage, enable proactive maintenance strategies, and enhance the overall safety and longevity of bridge infrastructure.
Supervisors
Publikasjoner
2024
-
Raviolo, Davide;
Civera, Marco;
Fragonara, Luca Zanotti.
(2024)
A Bayesian sampling optimisation strategy for finite element model updating.
Journal of Civil Structural Health Monitoring (JCSHM)
Vitenskapelig artikkel
2023
-
Raviolo, Davide;
Civera, Marco;
Fragonara, Luca Zanotti.
(2023)
A Comparative Analysis of Optimization Algorithms for Finite Element Model Updating on Numerical and Experimental Benchmarks.
Buildings
Vitenskapelig oversiktsartikkel/review
Tidsskriftspublikasjoner
-
Raviolo, Davide;
Civera, Marco;
Fragonara, Luca Zanotti.
(2024)
A Bayesian sampling optimisation strategy for finite element model updating.
Journal of Civil Structural Health Monitoring (JCSHM)
Vitenskapelig artikkel
-
Raviolo, Davide;
Civera, Marco;
Fragonara, Luca Zanotti.
(2023)
A Comparative Analysis of Optimization Algorithms for Finite Element Model Updating on Numerical and Experimental Benchmarks.
Buildings
Vitenskapelig oversiktsartikkel/review