Iterative hierarchical clustering algorithm for automated operational modal analysis

Recent developments in sensors and data processing made the structural health monitoring (SHM) sector expanding to big-data field, particularly when continuous long-term strategies are implemented. Nevertheless, main shortcomings are due to the identification and extraction of modal features. In fac...

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Bibliographic Details
Published in:Automation in construction Vol. 156; p. 105137
Main Authors: Romanazzi, A., Scocciolini, D., Savoia, M., Buratti, N.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2023
Subjects:
Iterative hierarchical clustering analysis
Machine learning
Sobol's indexes
SSI covariance driven
Structural health monitoring
Variance-based sensitivity analysis
Structural health monitoring
SSI covariance driven
Iterative hierarchical clustering analysis
Variance-based sensitivity analysis
Machine learning
Sobol's indexes
ISSN:0926-5805, 1872-7891
Online Access:Get full text
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Summary:Recent developments in sensors and data processing made the structural health monitoring (SHM) sector expanding to big-data field, particularly when continuous long-term strategies are implemented. Nevertheless, main shortcomings are due to the identification and extraction of modal features. In fact, although machine learning methods have been implemented to automate modal identification processes, intense user interaction and time-consuming procedures are still required, limiting the extensive use of these techniques. In order to provide a fully automated procedure capable of identifying and extracting modal properties from covariance driven SSI analyses, an innovative and flexible algorithm for Iterative Hierarchical Clustering Analysis (IHCA) is proposed. To evaluate the stability and robustness of the IHCA method, a Variance-Based Global sensitivity Analysis (VBGA) was performed considering a numerical and experimental case study. The outcomes demonstrated that the IHCA is stable in clustering the physical structural modes and selecting the most representative modal features. •An innovative iterative hierarchical clustering method is proposed to support automated modal identification of structures•The robustness of the algorithm is validated through variance-based sensitivity analyses performed on numerical and experimental data•The proposed algorithm is able to detect outliers in the identified modes despite the noise in the recorded accelerations•The proposed algorithm is reliable for automated continuous structural health monitoring and for supporting decision making
ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2023.105137