Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
Browse
3 results
Search Results
Article Citation - WoS: 18Citation - Scopus: 19Modified Frequency and Spatial Domain Decomposition Method Based on Maximum Likelihood Estimation(Elsevier, 2020) Hızal, ÇağlayanIn 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: 7Citation - Scopus: 9Frequency Domain Data Merging in Operational Modal Analysis Based on Least Squares Approach(Elsevier, 2020) Hızal, ÇağlayanAssembling 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 LtdArticle Citation - WoS: 15Citation - Scopus: 16A Mode Shape Assembly Algorithm by Using Two Stage Bayesian Fast Fourier Transform Approach(Academic Press Inc., 2019) Hızal, Çağlayan; Turan, Gürsoy; Aktaş, Engin; Ceylan, HasanOperational 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.