scholarly journals Online Modal Identification of Concrete Dams Using the Subspace Tracking-Based Method

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Lin Cheng ◽  
Fei Tong ◽  
Jie Yang ◽  
Dongjian Zheng
Keyword(s):  
Strong Motion ◽  
Arch Dam ◽  
Modal Identification ◽  
Subspace Identification ◽  
Time Varying ◽  
Subspace Tracking ◽  
Concrete Dams ◽  
Identification Procedure ◽  
Strong Motion Records ◽  
Stochastic Subspace Identification

To investigate the time-varying dynamic characteristics of concrete dams under the excitation of large earthquakes for online structural health monitoring and damage evaluation, an online modal identification procedure based on strong-motion records is proposed. The online modal identification of concrete dams is expressed as a subspace tracking problem, and a newly developed recursive stochastic subspace identification (RSSI) method based on the generalized yet another subspace tracker (GYAST) algorithm, which exploits both the accuracy of the subspace identification and fast computational capability, is used to extract the time-varying modal parameters of concrete dams during earthquakes. With the simulated vibration response records, a numerical example is used to verify the accuracy, robustness, and efficiency of the proposed GYAST-based, time-varying modal identification method. Then, the realistic strong-motion records of the Pacoima arch dam are analysed using the proposed modal identification procedure, and the time-varying characteristics of the concrete arch dam during three different earthquakes are analysed.

Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification

2017 ◽  
Vol 24 (20) ◽  
pp. 4781-4796 ◽  
Author(s):  
Wenchao Li ◽  
Viet-Hung Vu ◽  
Zhaoheng Liu ◽  
Marc Thomas ◽  
Bruce Hazel
Keyword(s):  
Data Driven ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Time Varying ◽  
Lumped Mass ◽  
State Matrix ◽  
Mass System ◽  
Stochastic Subspace Identification ◽  
Time Varying Systems ◽  
Using Data

This paper presents a method for the extraction of modal parameters for identification of time-varying systems using Data-Driven Stochastic Subspace Identification (SSI-DATA). In practical applications of SSI-DATA, both the modal parameters and computational ones are mixed together in the identified results. In order to differentiate the structural ones from computational ones, a new method based on the eigen-decomposition of the state matrix constructed in SSI-DATA is proposed. The efficiency of the proposed method is demonstrated through numerical simulation of a lumped-mass system and experimental test of a moving robot for extracting excited natural frequencies of the system.

scholarly journals OUTPUT-ONLY MODAL IDENTIFICATION OF TENSEGRITY STRUCTURES

2016 ◽  
Vol 8 (2) ◽  
pp. 52-64 ◽  
Author(s):  
Miniar Attig ◽  
Maher Abdelghani ◽  
Nabil ben Kahla
Keyword(s):  
Natural Frequencies ◽  
Modal Identification ◽  
Mode Shapes ◽  
Subspace Identification ◽  
Balanced Realization ◽  
Stochastic Subspace Identification ◽  
Tensegrity Systems ◽  
Stabilization Diagram ◽  
Subspace Algorithms ◽  
Output Only

Tensegrity systems are a special class of spatial reticulated structures that are composed of struts in compression and cables in tension. In this paper, the performance of stochastic subspace algorithms for modal identification of complex tensegrity systems is investigated. A sub-class algorithm of the Stochastic Subspace Identification family: the Balanced Realization Algorithm is investigated for modal identification of a tripod simplex structure and a Geiger dome. The presented algorithm is combined with a stabilization diagram with combined criteria (frequency, damping and mode shapes). It is shown that although the studied structures present closely spaced modes, the Balanced Realization Algorithm performs well and guarantees separation between closely-spaced natural frequencies. Modal identification results are validated through comparisons of the correlations (empirical vs. model based) showing effectiveness of the proposed methodology.

Experimental Modal Analysis of Jacket-Type Platforms Using Data-Driven Stochastic Subspace Identification Method

2012 ◽  
Author(s):  
Junfeng Xin ◽  
Sau-Lon James Hu ◽  
Huajun Li
Keyword(s):  
White Noise ◽  
Impact Loading ◽  
Modal Identification ◽  
Data Driven ◽  
Subspace Identification ◽  
Stochastic Subspace Identification ◽  
Gaussian White Noise Process ◽  
Using Data ◽  
Output Only ◽  
The Impact

Employing efficient techniques to accurately identify the modal parameters of new and aging offshore structures has been of interest to the offshore industry for decades. Early methods of modal identification were developed for the frequency domain. The new trend is to employ either input-output or output-only time-domain modal identification methods. Under the assumption that the excitation input is a zero-mean Gaussian white noise process, a modern output-only method that allows direct application to the response time series is the data-driven stochastic subspace identification (SSI-data) method. The main objective of this paper is to evaluate the performance of the SSI-data method using the test data measured from a physical model of a realistic offshore jacket-type platform. Response acceleration data associated with three different excitation mechanisms are investigated: impact loading, step relaxation and white noise ground motion. Although the SSI-data method has been theoretically developed, and often perceived to be only valid, for the ambient noise testing environment, it is shown in this study that the SSI-data method also performs well using data from either the impact loading or step relaxation tests.

Identification of Modal Parameters in a Scale Model for a Railway Bridge

2016 ◽  
Vol 16 (09) ◽  
pp. 1550059 ◽  
Author(s):  
Ladislao R. Ticona Melo ◽  
Ramon S. Y. R. C. Silva ◽  
Tulio N. Bittencourt ◽  
Luciano M. Bezerra
Keyword(s):  
Natural Frequencies ◽  
Numerical Study ◽  
Modal Identification ◽  
Scale Model ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Dynamic Parameters ◽  
Railway Bridge ◽  
Stochastic Subspace Identification

Obtaining the dynamic parameters of a bridge is essential to validation of the model used in the dynamic analysis of the bridge. In order to implement methodologies for damage detection, many experimental modal identification tests are performed for estimating the modal parameters of structures, including the natural frequencies and mode shapes. This paper presents an experimental and numerical study for finding the modal parameters of an aluminum model built to scale of a real railway bridge. Although, so far no test has been performed on the real railway bridge for comparison with the scale model, this paper seeks to apply the scale model experimental and numerical techniques for obtaining the dynamic parameters of the bridge. The software SAP2000 was used for the numerical analyses and for the experimental aspect, the circle fit and the stochastic subspace identification methods are used. Numerical and experimental results showed good correlation.

Time-Varying Output-Only Identification of a Cracked Beam

2009 ◽  
Vol 413-414 ◽  
pp. 643-650 ◽  
Author(s):  
A. Bellino ◽  
Luigi Garibaldi ◽  
Stefano Marchesiello
Keyword(s):  
Subspace Identification ◽  
Open Crack ◽  
Moving Mass ◽  
Time Varying ◽  
Cracked Beam ◽  
Stochastic Subspace Identification ◽  
Identification Method ◽  
Time Varying Systems ◽  
Output Only ◽  
Short Time

In this paper a time-varying identification method is presented, in order to detect the presence of an open crack in a beam with a moving mass travelling on it. The ratio between the considered moving mass and the total mass of the beam is high, thus the identified modal frequencies of the whole structure are time-varying. This situation often occurs when considering the dynamic interaction beetween a train and a bridge and specific identification tools must be used. It is shown that the identification method, referred to here as Short-Time Stochastic Subspace Identification, can give information about the presence of damage in case of time-varying systems.

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