Paper Information

Journal:   MODARES CIVIL ENGINEERING JOURNAL   APRIL 2017 , Volume 17 , Number 1 #F0062; Page(s) 53 To 64.
 
Paper: 

SYSTEM IDENTIFICATION OF ARCH DAMS USING STOCHASTIC SUBSPACE BASED ON STANDARD CORRELATION ANALYSIS

 
 
Author(s):  TARINEJAD REZA*, POURGHOLI MEHRAN, YAGHMAEI SABEGH SAMAN
 
* FACULTY OF CIVIL ENGINEERING, UNIVERSITY OF TABRIZ, TABRIZ, IRAN
 
Abstract: 

As a conventional method, the finite element model is used for static and dynamic analysis of structures such as dams and bridges. Nevertheless, these models are not able to describe the accurate behavior of structures against dynamic loads, because of some simplifications used in numerical modeling process, including loading, boundary conditions etc. Nowadays, modal testing is used to solve these problems. The dynamic tests, including forced, free and environmental vibration tests, are used in system identification of civil structures. Considering either unknown nature of inputs or unsuccessful steps of measuring them, some methods have been developed to analyze the results of dynamic tests which are based on measuring only output data and are known as operational modal analysis. Some of such methods are Peak Picking (PP), Frequency Domain Decomposition (FDD) and stochastic subspace methods. However, unknown nature of applied forces, the presence of environmental noise and measurement errors may result in some uncertainties within the results of these tests. In this article, a modal analysis is presented within a stochastic subspace which is among the most robust and accurate system identification techniques. In contrast to the previous methodologies, this analysis identifies dynamic properties in optimized space -instead of data space- by extracting ortho-normal vector of data space. Given the optimum nature of the proposed method, more accuracy may be served in detection and removal of unstable poles as well as high-speed analysis. In order to evaluate the proposed method in terms of civil systems detection, seismic and steady-state sinusoidal excitations were used. The former is selected from the most real and strong environmental vibrations and the latter is from the most precise forced vibration tests. In the first step, 2001 San Fernando earthquake data were analyzed using SSI-CCA and SSI-data methods. Data processing rate in the SSI-CCA method is almost twice as much as that in SSI-data method, and it is just because of processing in an optimum space while lowering the use of least squares method to compute system vector. Furthermore, there is one unstable pole in the results of the proposed method while 4 noisy characteristics were recognized in the results of SSI-Data method. Estimated damping ratio comprised the major difference observed in the results presented by above-mentioned methods. Modal damping ratio -estimated by the proposed method- were 60% closer to the previous results compared to those of the previous subspace method. Mode shapes of both subspace methods with MAC value of 92% and 75% for the first and the second modes, respectively, are well correlated with each other.
Due to the lack of access to the mode shape vectors of Alves’s method, it was not feasible to calculate the corresponding MAC value. In the following, forced vibration test results of Shahid-Rajaee Dam conducted by steady sinusoidal excitation in 2000 and analyzed by a method known as four spectral, are re-processed using the SSI-CCA method. As results indicate, by using the proposed method the first three modes, which were not on the preliminary results, are obtained. In addition, other modes are in good agreement with the results of the finite element method.

 
Keyword(s): HANKEL MATRIX, CANONICAL CORRELATION ANALYSIS, STOCHASTIC SUBSPACE, MODAL ANALYSIS
 
References: 
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