Multi-stage semi-automated methodology for modal parameters estimation adopting parametric system identification algorithms. (15th February 2022)

• Record Type: Journal Article
• Authors: Tronci, E.M.; De Angelis, M.; Betti, R.; Altomare, V.
• Is Part Of: Mechanical systems and signal processing. Volume 165(2022)
• Publication Date: 2022-02-15
• Subjects: Structural health monitoring -- Semi-automated operational modal analysis -- Parametric system identification -- Clustering -- Outlier analysis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108317
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: In recent years, a new research direction in structural condition assessment has been focusing on developing automated or semi-automated procedures to identify a structure's modal parameters from its response measurements. This is because long-term structural monitoring systems rely on the implementation of system identification methodologies that often involve the intervention of an expert user with an acquired experience in the field. This paper aims to offer a semi-automated methodology for extracting the modal parameters independently of the chosen parametric system identification technique with minimum user involvement in the parameter selection process. Here, the framework is applied to two different parametric system identification algorithms: Data-Driven Stochastic Subspace Identification (DD-SSI) and Output Only Observer Kalman Filter (O/O OKID). The procedure can be represented as a multi-stage strategy where unsupervised tools and three clustering options are offered to the user to reach a reliable estimate of the modal parameters. The proposed procedure is validated with an application in the operational modal analysis of an existing hospital structure located in Italy. The results demonstrated excellent accuracy and robust performance of the methodology, even in the presence of closely spaced modes. The proposed procedure helps to improve the data analysis process in continuous monitoring, where usually, the algorithm's parameters need to be constantly updated by the user. Highlights: Semi-automated operational modal analysis with low user interaction Methodology independent from the parametric system identification algorithm adopted Three clustering techniques available to the user: two k-means approaches and DBSCAN Application on the analysis of vibration data recorded on a RC hospital structure Validation using two system identification algorithms: DD-SSI and O/O OKID