Output-only Time and Frequency Domain system identification techniques are developed in this doctoral dissertation towards the challenging assessment of current structural dynamic properties of buildings from earthquake-induced structural response signals, at simultaneous heavy damping. Three different Operational Modal Analysis (OMA) techniques, namely a refined Frequency Domain Decomposition (rFDD) algorithm, an improved Data-Driven Stochastic Subspace Identification (SSI-DATA) procedure and a novel Full Dynamic Compound Inverse Method (FDCIM) are formulated and implemented within MATLAB, and exploited for the strong ground motion modal dynamic identification of selected buildings. First, the three OMA methods are validated by the adoption of synthetic earthquake-induced structural response signals, generated from numerical integration on benchmark linear shear-type frames. Then, real seismic response signals are effectively processed, by getting even closer to real Earthquake Engineering identification scenarios. In the end, the three OMA methods are systematically applied and compared. The present thesis demonstrates the reliability and effectiveness of such advanced OMA methods, as convenient output-only modal identification tools for Earthquake Engineering and Structural Health Monitoring purposes.

(2017). Time and Frequency Domain output-only system identification from earthquake-induced structural response signals [doctoral thesis - tesi di dottorato]. Retrieved from http://hdl.handle.net/10446/77137

Time and Frequency Domain output-only system identification from earthquake-induced structural response signals

PIOLDI, Fabio
2017-05-31

Abstract

Output-only Time and Frequency Domain system identification techniques are developed in this doctoral dissertation towards the challenging assessment of current structural dynamic properties of buildings from earthquake-induced structural response signals, at simultaneous heavy damping. Three different Operational Modal Analysis (OMA) techniques, namely a refined Frequency Domain Decomposition (rFDD) algorithm, an improved Data-Driven Stochastic Subspace Identification (SSI-DATA) procedure and a novel Full Dynamic Compound Inverse Method (FDCIM) are formulated and implemented within MATLAB, and exploited for the strong ground motion modal dynamic identification of selected buildings. First, the three OMA methods are validated by the adoption of synthetic earthquake-induced structural response signals, generated from numerical integration on benchmark linear shear-type frames. Then, real seismic response signals are effectively processed, by getting even closer to real Earthquake Engineering identification scenarios. In the end, the three OMA methods are systematically applied and compared. The present thesis demonstrates the reliability and effectiveness of such advanced OMA methods, as convenient output-only modal identification tools for Earthquake Engineering and Structural Health Monitoring purposes.
31-mag-2017
29
2015/2016
INGEGNERIA E SCIENZE APPLICATE
RIZZI, Egidio
Pioldi, Fabio
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