Computer Science > Computational Engineering, Finance, and Science
[Submitted on 8 Nov 2024]
Title:Fast Stochastic Subspace Identification of Densely Instrumented Bridges Using Randomized SVD
View PDF HTML (experimental)Abstract:The rising number of bridge collapses worldwide has compelled governments to introduce predictive maintenance strategies to extend structural lifespan. In this context, vibration-based Structural Health Monitoring (SHM) techniques utilizing Operational Modal Analysis (OMA) are favored for their non-destructive and global assessment capabilities. However, long multi-span bridges instrumented with dense arrays of accelerometers present a particular challenge, as the computational demands of classical OMA techniques in such cases are incompatible with long-term SHM. To address this issue, this paper introduces Randomized Singular Value Decomposition (RSVD) as an efficient alternative to traditional SVD within Covariance-driven Stochastic Subspace Identification (CoV-SSI). The efficacy of RSVD is also leveraged to enhance modal identification results and reduce the need for expert intervention by means of 3D stabilization diagrams, which facilitate the investigation of the modal estimates over different model orders and time lags. The approach's effectiveness is demonstrated on the San Faustino Bridge in Italy, equipped with over 60 multiaxial accelerometers.
Submission history
From: Enrique García Macías Mr [view email][v1] Fri, 8 Nov 2024 12:12:21 UTC (9,671 KB)
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.