Civil Engineering / İnşaat Mühendisliği

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

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Author: search.filters.author.Hızal, ÇağlayanSubject: search.filters.subject.Multiple setups

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  • Article
    Citation - WoS: 15
    Citation - Scopus: 16
    A Mode Shape Assembly Algorithm by Using Two Stage Bayesian Fast Fourier Transform Approach
    (Academic Press Inc., 2019) Hızal, Çağlayan; Turan, Gürsoy; Aktaş, Engin; Ceylan, Hasan
    Operational modal analysis may require identifying global modal shapes by using multiple setup measurements. For this purpose, various algorithms have been developed which make use of the Bayesian approach to estimate the global mode shapes. The main motivation of the available Bayesian approaches is based on the estimation of the optimal global mode shape vector directly from Fast Fourier Transform data or assembling the local mode shapes that are identified in the individual setups by using Gaussian approximation. In this study, the two-stage Bayesian Fast Fourier Transform Approach which is originally applied to single setups is implemented to multiple setup problems for well separated modes. Analytically it is shown that the resulting formulation is the same for the mode shape assembly by using the Gaussian approximation. In addition, the weights of individual setups in the global mode shape vector is analytically calculated which depend on the Hessian matrix for local mode shapes. To validate the proposed methodology, a numerical example that considers setup-to-setup variability of modal signal-noise ratios is presented. For comparison purposes a ten-story shear frame model is experimentally investigated, and the measurements of a benchmark bridge structure are considered in the verification of the current procedure. (C) 2019 Elsevier Ltd. All rights reserved.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 5
    Pre-Identification Data Merging for Multiple Setup Measurements With Roving References
    (Springer Verlag, 2020) Ceylan, Hasan; Turan, Gürsoy; Hızal, Çağlayan
    One-time operational modal analysis (OMA) of large civil structures requires measurements of the vibrations, which, according to the number of channels to be measured, are generally expensive and arduous to obtain. In this study, identification of modal parameters of civil structures has been investigated by using multiple setups with a roving reference channel. In this manner, a limited amount of equipment becomes sufficient for OMA of structures. The procedure consists of a transformation function between measurement setups, which transforms all measured data to the time frame of a selected reference setup. To illustrate the procedure, an existing 10 story laboratory shear frame model is considered. A numerical and an experimental investigation have been carried out to identify its modal characteristics. The validity of the procedure has been explained in detail by making use of a coherence function in-between the multi-setup measurements. According to the results, OMA by using only a few sensors with the performed procedure can be equivalent to OMA by using a full measurement setup. Against a common believe, the results of this study reveal that synchronization among the setups does not prominently affect the identification results.
  • Article
    Citation - WoS: 29
    Citation - Scopus: 29
    A Two-Stage Bayesian Algorithm for Finite Element Model Updating by Using Ambient Response Data From Multiple Measurement Setups
    (Academic Press, 2020) Hızal, Çağlayan; Turan, Gürsoy
    This study presents a two-stage Bayesian finite element model updating procedure by using acceleration response measurements obtained from multiple setups. In the presented methodology, parametric uncertainties for the modal parameters are estimated by using the Bayesian Fast Fourier Transform Approach (BFFTA). Different from the previous Bayesian methods, a block diagonal covariance matrix is modeled for prior estimation of measured modal parameters. In addition, the modelling error in the eigenvalue equations is considered as soft constraints to be updated. Numerical and experimental studies are presented to validate the proposed method. The effect of soft constraints on the identification results as well as their posterior uncertainties are investigated. According to the results, it is shown that the proposed methodology can identify the most probable finite element model parameters with high level of accuracy. In addition, the posterior uncertainties obtained by the proposed procedure are significantly small when compared to the methods that consider rigid constraints for prediction and/or modelling error. (C) 2019 Elsevier Ltd. All rights reserved.