Automated long-term dynamic monitoring using hierarchical clustering and adaptive modal tracking: validation and applications

Giacomo Zonno, R. Aguilar, Ruben Boroschek, P. B. Lourenço

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Historical buildings demand constant surveying because anthropogenic (e.g., use, pollution or traffic vibration) and natural or environmental hazards (e.g., environmental changes or earthquakes) can endanger their existence and safety. Particularly, in the Andean region of South America, earthen historical constructions require special attention and investigation due to the high seismic hazard of the area next to the Pacific coast. Structural Health Monitoring (SHM) can provide useful, real-time information on the condition of these buildings. In SHM, the implementation of automatic tools for feature extraction of modal parameters is a crucial step. This paper proposes a methodology for the automatic identification of the structural modal parameters. An innovative and multi-stage approach for the automatic dynamic monitoring is presented. This approach uses the Data-Driven Stochastic Subspace Identification method complemented by hierarchical clustering for automatic detection of the modal parameters, as well as an adaptive modal tracking procedure for providing a clear visualization of long-term monitoring results. The proposed methodology is first validated in data acquired in an emblematic sixteenth century historical building: the monastery of Jeronimos in Portugal. After proving its efficiency, the algorithm is used to process almost 5000 events containing data acquired in the church of Andahuaylillas, a sixteenth century adobe building located in Cusco, Peru. The results in these cases demonstrate that accurate estimation of predominant modal parameters is possible in those complex structures even if relatively few sensors are installed.
Original languageSpanish
Pages (from-to)791-808
Number of pages18
JournalJournal of Civil Structural Health Monitoring
StatePublished - 1 Nov 2018

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