Sharif Digital Repository

In this study, a frequency domain model updating method is presented using power spectral density (PSD) data. It uses the sensitivity of PSD function with respect to the unknown structural parameters through a decomposed form of transfer function. The stiffness parameters are captured with high accuracy through solving the sensitivity equations utilizing the least square approach. Using numerically noise polluted data, the model updating results of a truss model prove robustness of the method against measurement and mass modelling errors. Results prove the capabilities of the method for parameter estimation using highly noise polluted data of low ranges of excitation frequency. Copyright ©... 

Offshore jacket platforms are one of the most motivating structures for damage detection due to their importance and productivity. In this study, the application of finite element model updating in damage detection of an offshore jacket platform is investigated. The objective function of this method is based on the measured and analytical modal data, including natural frequencies and mode shapes. However, the measured data is expected to be noisy. Also, to avoid obtaining false damage results, a penalty term is added to the objective function. To update the model, genetic algorithm is utilized as a robust global searching tool. Afterward, the efficiency of this method is evaluated on several... 

In this paper, a sensitivity-based finite element (FE) model updating method using singular value decomposition (SVD) of frequency response function (FRF) is introduced. An exact sensitivity equation is proposed by incorporating measured responses of a damaged structure in the mathematical formulations. A set of incompletely measured natural frequencies of a damaged structure and mode shapes of the intact structure are used to deal with incomplete measurement without the implementation of FE model reduction or data expansion algorithms. The insights provided from the variation of SVD of transfer functions are used for the selection of proper updating frequency ranges. The appropriate... 

Herein, we propose a method based on the existing second-order blind identification of underdetermined mixtures technique for identifying the modal characteristics - namely, natural frequencies, damping ratio, and real-valued partial mode shapes of all contributing modes - of structures with a limited number of sensors from recorded free/ambient vibration data. In the second-order blind identification approach, second-order statistics of recorded signals are used to recover modal coordinates and mode shapes. Conventional versions of this approach require the number of sensors to be equal to or greater than the number of active modes. In the present study, we first employ a parallel factor... 

The accurate and precise computational models in order to predict the hole cleaning process is one of the helpful assets in drilling industries. Besides the bulk properties such as the flow velocity, particles average size, cleaning fluid properties, etc., that will affect the cleaning process, there is an unanswered question about the microscopic properties of the particles, particularly those which determines the contact characteristics: Do those play a major role or not? The rudimentary answer is not. The first purpose of the present work is to answer this question via a developed computational fluid dynamics coupled with discrete element method (CFD–DEM) in which the six unknown rolling... 

A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics. The proposed framework provides a reliable approach to account for the variability of the inference results observed when using different data sets. The proposed formulation is compared with a state-of-the-art Bayesian approach using numerical and experimental examples. The results indicate that the hierarchical Bayesian framework provides a more realistic account of the uncertainties, whereas the non-hierarchical Bayesian approach severely... 

Digital music technologies enable users to create and use large collections of music. One of the desirable features for users is the ability to automatically organize the collection and search in it. One of the operations that they need is automatic identification of tracks' artists. This operation can be used to automatically classify new added tracks to a collection. Additionally, the user can use this operation to identify the artist of an unknown track. The artist name of a track can help the user find similar music. In this paper, we introduce a fast and scalable system that can automatically identify the artist of music tracks. This system is creating a signature for each track that is... 

A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics. The proposed framework provides a reliable approach to account for the variability of the inference results observed when using different data sets. The proposed formulation is compared with a state-of-the-art Bayesian approach using numerical and experimental examples. The results indicate that the hierarchical Bayesian framework provides a more realistic account of the uncertainties, whereas the non-hierarchical Bayesian approach severely... 

In this paper, a new sensitivity-based model updating method is presented based on the changes of principal components (PCs) of frequency response function (FRF). Structural damage estimation, identification of damage location and severity, is conducted by an innovative sensitivity relation. The sensitivity relation is derived by incorporating PC analysis (PCA) data obtained from the incomplete measured structural responses in a mathematical formulation and is then solved by the least square method. In order to demonstrate the performance of the proposed method, it is applied to a truss and a frame model. The results prove the ability of the method as a robust damage detection algorithm in... 

In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconceptions in the Bayesian framework since it is robust with respect to the modeling assumptions and the observed data. Rather, this issue has deep roots in users’ inability to develop an appropriate class of probabilistic models. This paper bridges this significant gap, introducing a novel Bayesian hierarchical setting, which breaks time-history vibration responses into several segments so as to capture and identify the variability of inferred parameters over the...