Bayesian data fusion approach for InSAR and topographic bridge displacement monitoring

Daniel Tonelli; Mattia Zini; Lucia Simeoni; Alfredo Rocca; Daniele Perissin; Carlo Costa; David Quattrociocchi; Daniele Zonta

doi:10.1117/12.2692349

9 May 2024 Bayesian data fusion approach for InSAR and topographic bridge displacement monitoring

Daniel Tonelli, Mattia Zini, Lucia Simeoni, Alfredo Rocca, Daniele Perissin, Carlo Costa, David Quattrociocchi, Daniele Zonta

Author Affiliations +

Proceedings Volume 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024; 129490O (2024) https://doi.org/10.1117/12.2692349
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Abstract

This paper introduces a Bayesian data fusion methodology for the monitoring of bridge displacements, employing a synergistic combination of satellite Interferometric Synthetic Aperture Radar (InSAR) and topographic measurements taken in free configuration. Focused on the case study of the Belprato 2 Viaduct, which is affected by a slow-moving landslide, this research demonstrates the potential of integrating diverse data sources to overcome the limitations posed by these monitoring techniques considered as alone. Our approach leverages the frequency and the remote, non-intrusive nature of InSAR technology and the accuracy of topographic surveys to obtain a high-resolution, three-dimensional bridge displacements caused by the landslide and temperature variations. The Bayesian framework facilitates the optimal fusion of these datasets, accounting for their respective uncertainties and different temporal resolutions. Moreover, it allows to include the information a priori on the landslide movements resulting for previous geological and geotechnical studies. The results from this study reveal significant improvements in the accuracy and reliability of displacement measurements, highlighting the benefits of data fusion for structural health monitoring. This paper highlights the importance of innovative monitoring solutions in the context of aging infrastructure, increasing environmental and traffic challenges, and complex topographical settings. Future directions for research include the exploration of real-time monitoring datasets and the integration of additional data types.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Daniel Tonelli, Mattia Zini, Lucia Simeoni, Alfredo Rocca, Daniele Perissin, Carlo Costa, David Quattrociocchi, and Daniele Zonta "Bayesian data fusion approach for InSAR and topographic bridge displacement monitoring", Proc. SPIE 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024, 129490O (9 May 2024); https://doi.org/10.1117/12.2692349

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE