Presentation + Paper
19 April 2017 Damage location and quantification of a pretensioned concrete beam using stochastic subspace identification
Alessandro Cancelli, Laura Micheli, Simon Laflamme, Alice Alipour, Sri Sritharan, Filippo Ubertini
Author Affiliations +
Abstract
Stochastic subspace identification (SSID) is a first-order linear system identification technique enabling modal analysis through the time domain. Research in the field of structural health monitoring has demonstrated that SSID can be used to successfully retrieve modal properties, including modal damping ratios, using output-only measurements. In this paper, the utilization of SSID for indirectly retrieving structures’ stiffness matrix was investigated, through the study of a simply supported reinforced concrete beam subjected to dynamic loads. Hence, by introducing a physical model of the structure, a second-order identification method is achieved. The reconstruction is based on system condensation methods, which enables calculation of reduced order stiffness, damping, and mass matrices for the structural system. The methods compute the reduced order matrices directly from the modal properties, obtained through the use of SSID. Lastly, the reduced properties of the system are used to reconstruct the stiffness matrix of the beam. The proposed approach is first verified through numerical simulations and then validated using experimental data obtained from a full-scale reinforced concrete beam that experienced progressive damage. Results show that the SSID technique can be used to diagnose, locate, and quantify damage through the reconstruction of the stiffness matrix.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alessandro Cancelli, Laura Micheli, Simon Laflamme, Alice Alipour, Sri Sritharan, and Filippo Ubertini "Damage location and quantification of a pretensioned concrete beam using stochastic subspace identification", Proc. SPIE 10169, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017, 1016917 (19 April 2017); https://doi.org/10.1117/12.2261825
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Cited by 1 scholarly publication.
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KEYWORDS
Matrices

Data modeling

Sensors

Stochastic processes

Structural health monitoring

Particles

Damage detection

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