scholarly journals Application of Improved Combined Deterministic-Stochastic Subspace Algorithm in Bridge Modal Parameter Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peng Wen ◽  
Inamullah Khan ◽  
Jie He ◽  
Qiaofeng Chen
Keyword(s):  
Parameter Identification ◽  
Clustering Algorithm ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Fuzzy C Means ◽  
Subspace Algorithm ◽  
Bridge Structures ◽  
Fuzzy C Means Clustering ◽  
Online Tracking

Modal parameter identification is considered to be one of the most important tasks in structural health monitoring because it provides a reliable reference for structural vibration control, damage severity, and operational state. Moreover, at present, the combined deterministic-stochastic subspace algorithm is cogitated as one of the key algorithms in the modal parameter identification, which is why it is widely used in the modal parameter identification of bridge structures. In this paper, a novel method is proposed, which is a time-domain identification algorithm, based on sliding window-fuzzy C-means clustering algorithm-combined with deterministic-stochastic subspace identification (SC-CDSI), to achieve online intelligent tracking and identification of modal parameters for nonlinear time-varying structures. First of all, to realize the online tracking and identification process, it is necessary to divide the input and output signal of the nonlinear time-varying structure by windowing; for that, to determine the window function, window size and window step length according to the characteristics of the signal are analyzed. Secondly, in order to satisfy the intelligent identification of effective modals in stability diagram, the fuzzy C-means clustering algorithm is kept as a base, whereas frequency, damping ratio, and modal shapes serve as clustering elements, applied to fuzzy C-means clustering algorithm, and then the intelligent selection of effective modals is achieved. Finally, a shaking table test bridge is used as a modal parameter identification in lab, and its results are compared with the MIDAS finite element results. The compared results show that the proposed SC-CDSI identification algorithm can accurately achieve the intelligent identification of online tracking of the structural frequency, and the identification results are reliable to be used in real-life bridge structures.

scholarly journals Time-Varying Modal Parameters Identification by Subspace Tracking Algorithm and Its Validation Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Haotian Zhou ◽  
Kaiping Yu ◽  
Yushu Chen ◽  
Rui Zhao ◽  
Yunhe Bai
Keyword(s):  
Parameter Identification ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Post Process

This article presents a time-varying modal parameter identification method based on the novel information criterion (NIC) algorithm and a post-process method for time-varying modal parameter estimation. In the practical application of the time-varying modal parameter identification algorithm, the identified results contain both real modal parameters and aberrant ones caused by the measurement noise. In order to improve the quality of the identified results as well as sifting and validating the real modal parameters, a post-process procedure based on density-based spatial clustering of applications with noise (DBSCAN) algorithm is introduced. The efficiency of the proposed approach is first verified through a numerical simulation of a cantilever Euler-Bernoulli beam with a time-varying mass. Then the proposed approach is experimentally demonstrated by composite sandwich structure in a time-varying high temperature environment. The identified results illustrate that the proposed approach can obtain real modal frequencies in low signal-to-noise ratio (SNR) scenarios.

scholarly journals Modal parameters identification of bridge by improved stochastic subspace identification method with Grubbs criterion

2021 ◽  
Vol 54 (3-4) ◽  
pp. 457-464
Author(s):  
Yulin Zhou ◽  
Xulei Jiang ◽  
Mingjin Zhang ◽  
Jinxiang Zhang ◽  
Hao Sun ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Parameter Identification ◽  
Damping Ratio ◽  
Wind Tunnel Test ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification

In the wind tunnel test of a long-span bridge model, to ensure that the dynamic characteristics of the model can satisfy the test design requirements, it is particularly important to accurately identify the modal parameters of the model. First, the stochastic subspace identification algorithm was used to analyze the modal parameters of the model in the wind tunnel test; then, Grubbs criterion was introduced to effectively eliminate outliers in the damping ratio matrix. Stochastic subspace identification algorithm with Grubbs criterion improved the accuracy of the modal parameter identification and the ability to determine system matrix order and prevented the modal omissions caused by determining the stable condition of the damping ratio in the stability diagram. Finally, Oujiang Bridge was used as an example to verify the stochastic subspace identification algorithm with Grubbs criterion and compare with the results of the finite element method. The example shows that the improved method can be effectively applied to the modal parameter identification of bridges.

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