Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Publisher: search.filters.publisher.ElsevierSubject: search.filters.subject.Modal identification

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  • Article
    Citation - WoS: 18
    Citation - Scopus: 19
    Modified Frequency and Spatial Domain Decomposition Method Based on Maximum Likelihood Estimation
    (Elsevier, 2020) Hızal, Çağlayan
    In this study, a Modified Frequency and Spatial Domain Decomposition (MFSDD) technique is developed for modal parameter identification, using output-only response measurements. According to the presented procedure, the most probable power spectral density matrix of the measured response (output PSD) is updated by a maximum likelihood estimation based on the observed data. Different from the available Frequency Domain Decomposition (FDD) techniques, a prediction error term which is associated with the measurement noise and modelling errors is included in the proposed methodology. In this context, a detailed discussion is provided from various aspects for the effect of measurement noise and modelling errors on the parameter estimation quality. Two numerical and two experimental analysis are conducted in order to demonstrate the effectiveness and accuracy of the proposed methodology under some extreme effects. The obtained results indicate that the proposed method shows very good performance in modal parameter estimation in case of noisy measurements.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 9
    Frequency Domain Data Merging in Operational Modal Analysis Based on Least Squares Approach
    (Elsevier, 2020) Hızal, Çağlayan
    Assembling of multi-setup measurements emerges as a challenging problem in the structural health monitoring applications and may cause some important issues in the estimation of global modal parameters such as frequency, damping ratio and modal shape vector. To overcome this problem, a novel frequency domain pre-identification data merging method is proposed in this study. In the proposed methodology, to obtain a single measurement set, a least squares approach is employed resulting in a global response that is scaled from the multi-setup data. For the verification of the proposed merging procedure, one numerical, two experimental studies and one real data application have been conducted. The results obtained from the numerical, experimental and real data analysis indicate that the presented methodology provides rather high-quality estimations for multi-setup measurement problems. © 2020 Elsevier Ltd