Davide Raviolo
About
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
Publications
2026
-
Raviolo, Davide;
Øiseth, Ole Andre;
Kvåle, Knut Andreas;
Petersen, Øyvind Wiig.
(2026)
Automated Novelty Detection under Environmental and Operational Variability in Vibration-Based Structural Health Monitoring.
Norges teknisk-naturvitenskapelige universitet
Doctoral thesis
-
Raviolo, Davide;
Kvåle, Knut Andreas;
Petersen, Øyvind Wiig;
Øiseth, Ole Andre.
(2026)
Latent-Factor State-Space Modelling with Exogenous Inputs for Novelty Detection in Automated Modal SHM.
NDT.net
Academic article
-
Raviolo, Davide;
Kvåle, Knut Andreas;
Petersen, Øyvind Wiig;
Øiseth, Ole Andre.
(2026)
Unsupervised damage detection using singular-vector feature maps and 2D convolutional autoencoders: Validation on a multi-state shear-frame experiment.
Structural Health Monitoring
Academic article
2025
-
Raviolo, Davide;
Kvåle, Knut Andreas;
Petersen, Øyvind Wiig;
Øiseth, Ole Andre.
(2025)
Automated mode identification and tracking via frequency domain decomposition.
Journal of Sound and Vibration
Academic article
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)
Academic article
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
Academic literature review
Journal publications
-
Raviolo, Davide;
Kvåle, Knut Andreas;
Petersen, Øyvind Wiig;
Øiseth, Ole Andre.
(2026)
Latent-Factor State-Space Modelling with Exogenous Inputs for Novelty Detection in Automated Modal SHM.
NDT.net
Academic article
-
Raviolo, Davide;
Kvåle, Knut Andreas;
Petersen, Øyvind Wiig;
Øiseth, Ole Andre.
(2026)
Unsupervised damage detection using singular-vector feature maps and 2D convolutional autoencoders: Validation on a multi-state shear-frame experiment.
Structural Health Monitoring
Academic article
-
Raviolo, Davide;
Kvåle, Knut Andreas;
Petersen, Øyvind Wiig;
Øiseth, Ole Andre.
(2025)
Automated mode identification and tracking via frequency domain decomposition.
Journal of Sound and Vibration
Academic article
-
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)
Academic article
-
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
Academic literature review
Student thesis or dissertation
-
Raviolo, Davide;
Øiseth, Ole Andre;
Kvåle, Knut Andreas;
Petersen, Øyvind Wiig.
(2026)
Automated Novelty Detection under Environmental and Operational Variability in Vibration-Based Structural Health Monitoring.
Norges teknisk-naturvitenskapelige universitet
Doctoral thesis