Imaging · dataset · 2025
Ponte Moesa Campagnola: A bridge benchmark for structural identification under controlled damage progression
Listed in ETH Zürich Research Collection
In addressing the challenge of ageing infrastructure, continuous structural monitoring has figured prominently in the development of tools for risk management and life-cycle prognostic strategies.
Description
However, a primary challenge lies in robustly quantifying structural condition using, typically indirect, monitoring observations. In recent years, a vast number of various data-driven or hybrid analysis methods have been proposed, targeting different levels of the so-called Rytter’s hierarchy of damage identification.
The more advanced identification tasks, relating to a more precise characterization (e.g., location and quantity) of damage are nontrivial to address. A primary difficulty in this respect relates to lack of labelled data corresponding to predefined damage states of the structure of interest. This implies that for most practical contexts, such structural identification ought to be achieved in an unsupervised manner.
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This work presents a new full-scale bridge experimental benchmark which can serve as a case study for verification and validation of damage identification schemes. The Ponte Moesa Campagnola (PMC) benchmark structure, which was decommissioned in 2019, represents a typical bridge structure of the Swiss Roadway Network. The bridge was subjected to a 4-day monitoring campaign, during which controlled damage progression scenarios were implemented.
The monitored quantities comprised a multimodal mix of both acceleration and strain information, continually recorded during the 4-day campaign. The reported results demonstrate the potential of this data set to serve for structural identification and damage detection (DD) purposes. To this end, we present—merely as a viability study—the successful implementation of three damage-sensitive features (DSFs).
The goal was to describe the data set and introduce it for further study, testing, and validation of emerging DD algorithms tailored for full-scale structural health monitoring (SHM) utilization.
Links
Where it is published
- ETH Zürich Research Collection record hdl.handle.net/20.500.11850/716850 ↗
landing page · from research collection ethz ch
- DOI doi.org/10.3929/ethz-b-000716850 ↗
DOI / persistent id · from research collection ethz ch
Catalogue records · 1
- OAI-PMH record research-collection.ethz.ch/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Awww.… ↗
metadata API · from research collection ethz ch
Topics
- From keywords
- Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Materials Science · Mathematics & Statistics · Medicine & Health · Physics · Psychology & Behavioral Science · Social Science
- Inferred from text
- Civil engineering 74%
Related
Provenance · 1 source records, 19 field assertions
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|---|---|---|---|
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