A recent collaborative project between Politecnico di Milano and Regione Lombardia included the definition of guidelines for the structural monitoring of bridges and the subsequent implementation in nine pilot bridges. Among the different monitoring strategies suggested in those guidelines, the one based on vibration monitoring and automated Operational Modal Analysis (OMA) is herein considered. The paper describes a MATLAB toolbox, aimed atautomatizing as much as possible the OMA-based Structural Health Monitoring process. In short, the developed toolbox is denoted as DYMOND, with the acronym standing for DYnamic MOnitoring and Novelty Detection. The DYMOND software includes various toolkits: (a) automated modal identification based on the Stochastic Subspace Identification (SSI); (b) fully automated modal parameters tracking; (c) minimization of environmental and operational variability (EOV) and (d) novelty analysis for anomaly detection.The application of the above tools is briefly exemplified based on data collected on a dynamic monitoring of a historical reinforced concrete bridge.
DYMOND: A Matlab Toolbox for the Dynamic Monitoring of Bridges According to the Lombardia Regional Guidelines
Gentile, Carmelo;Pirrò, Marco;Borlenghi, Paolo
2024-01-01
Abstract
A recent collaborative project between Politecnico di Milano and Regione Lombardia included the definition of guidelines for the structural monitoring of bridges and the subsequent implementation in nine pilot bridges. Among the different monitoring strategies suggested in those guidelines, the one based on vibration monitoring and automated Operational Modal Analysis (OMA) is herein considered. The paper describes a MATLAB toolbox, aimed atautomatizing as much as possible the OMA-based Structural Health Monitoring process. In short, the developed toolbox is denoted as DYMOND, with the acronym standing for DYnamic MOnitoring and Novelty Detection. The DYMOND software includes various toolkits: (a) automated modal identification based on the Stochastic Subspace Identification (SSI); (b) fully automated modal parameters tracking; (c) minimization of environmental and operational variability (EOV) and (d) novelty analysis for anomaly detection.The application of the above tools is briefly exemplified based on data collected on a dynamic monitoring of a historical reinforced concrete bridge.File | Dimensione | Formato | |
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IOMAC2024_476-487.pdf
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