Journal
STRUCTURAL CONTROL & HEALTH MONITORING
Volume 29, Issue 10, Pages -Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/stc.3028
Keywords
automated operational modal analysis; damage detection; machine learning; masonry arch bridge; operational modal analysis; structural health monitoring
Funding
- Politecnico di Torino within the CRUI-CARE Agreement
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Structural health monitoring is an important research topic in civil, mechanical and aerospace engineering. Output-only techniques are suitable for large civil structures and can be automated using artificial intelligence and machine learning techniques for interpretation.
Structural health monitoring (SHM) is one of the main research topics in civil, mechanical and aerospace engineering. In this regard, modal parameters and their trends over time can be used as features and indicators of damage occurrence and growth. However, for practical reasons, output-only techniques are particularly suitable for the system identification (SI) of large civil structures and infrastructures, as they do not require a controlled source of input force. In this context, these approaches are typically referred to as operational modal analysis (OMA) techniques. However, the interpretation of the OMA identifications is a labour-intensive task, which could be better automated with artificial intelligence and machine learning (ML) techniques. In particular, clustering and cluster analysis can be used to group unlabelled datasets and interpret them. In this study, a novel multi-stage clustering algorithm for automatic OMA (AOMA) is tested and validated for SHM applications-specifically, for damage detection and severity assessment-to a masonry arch bridge. The experimental case study involves a 1:2 scaled model, progressively damaged to simulate foundation scouring at the central pier.
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