Bayesian model updating of a large-span steel tied arch bridge: an experimental study

2021 ◽  
Author(s):  
Lanxin Luo ◽  
Huaqiang Zhong ◽  
Ye Xia ◽  
Limin Sun
Keyword(s):  
Experimental Study ◽  
Sensitivity Analysis ◽  
Bayesian Model ◽  
Model Updating ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Fe Model ◽  
Large Span ◽  
Vibration Data ◽  
Tied Arch Bridge

<p>In this paper, a large-span steel tied arch bridge's Bayesian FEMU is carried out based on the ambient vibration data. Firstly, the ERA method is used for modal identification. Then, the benchmark FE model of this bridge is established. Based on the sensitivity analysis, six updating parameters significantly affecting the natural frequency are selected. Subsequently, the objective function of the FEMU is established, and the DRAM algorithm is utilized to simulate the parameter samples conforming to the posterior distribution. Finally, the uncertainty analysis of the updated items is carried out. After FEMU, the results show that the model's frequency uncertainty is reduced, and the theoretical frequencies are highly consistent with the identified frequencies.</p>

Ambient Vibration Test, Modal Identification and Structural Model Updating Following Bayesian Framework

2015 ◽  
Vol 15 (07) ◽  
pp. 1540024 ◽  
Author(s):  
J. Yang ◽  
H. F. Lam ◽  
J. Hu
Keyword(s):  
Bayesian Model ◽  
Structural Model ◽  
Model Updating ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Modal Parameters ◽  
Vibration Test ◽  
Engineering Structures ◽  
Ambient Vibration Test ◽  
Vibration Data

Structural health monitoring (SHM) of civil engineering structures based on vibration data includes three main components: ambient vibration test, modal identification and model updating. This paper discussed these three components in detail and proposes a general framework of SHM for practical application. First, a fast Bayesian modal identification method based on Fast Fourier Transform (FFT) is introduced for efficiently extracting modal parameters together with the corresponding uncertainties from ambient vibration data. A recently developed Bayesian model updating method using Markov chain Monte Carlo simulation (MCMCS) is then discussed. To illustrate the performance of the proposed modal identification and model updating methods, a scale-down transmission tower is investigated. Ambient vibration test is conducted on the target structure to obtain modal parameters. By using the measured modal parameters, model updating is carried out. The MCMC-based Bayesian model updating method can efficiently evaluate the posterior marginal PDFs of the uncertain parameters without calculating high-dimension numerical integration, which provides posterior uncertainties for the target systems.

scholarly journals Automated band selection for Bayesian FFT modal identification Based on RetinaNet

2021 ◽  
Vol 2078 (1) ◽  
pp. 012058
Author(s):  
Chen Wang ◽  
Zhilin Xue ◽  
Yipeng Su ◽  
Binbin Li
Keyword(s):  
Operation Time ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Band Selection ◽  
Data Sets ◽  
Civil Structures ◽  
Frequency Bands ◽  
Operational Conditions ◽  
Vibration Data

Abstract Bayesian FFT algorithm is a popular method to identify modal parameters, e.g., modal frequencies, damping ratios, and mode shapes, of civil structures under operational conditions. It is efficient and provides the identification uncertainty in terms of posterior distribution. However, in utilizing the Bayesian FFT algorithm, it is tedious to manually select frequency bands and initial frequencies. This step requires professional knowledge and costs most of time, which prevents the automation of Bayesian FFT algorithm. Regarding the band selection as an object detection problem, we design a band selection network based on the RetinaNet to automatically select frequency bands and a peak prediction network to predict the initial frequencies. The designed networks are trained using the singular value spectrum of measured ambient vibration data and verified by various data sets. It can achieve the human accuracy with much less operation time, and thus provides a corner stone for the automation of Bayesian FFT algorithm.

FE Model Updating for Health Monitoring of Structures and its Experimental Verification by Damage Detection

2006 ◽  
Vol 321-323 ◽  
pp. 268-272 ◽  
Author(s):  
Gwang Hee Heo ◽  
Joon Ryong Jeon ◽  
Chin Ok Lee ◽  
Gui Lee ◽  
Woo Sang Lee
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Model Updating ◽  
Basic Structure ◽  
Ambient Vibration ◽  
Fe Model ◽  
Error Matrix ◽  
Error Ratio ◽  
Before And After ◽  
Fe Model Updating

This paper presents an effective method of FE model updating for health monitoring of structures by applying ambient vibration. And this method is experimented through damage detection and proved to be valid. Experiment about ambient vibration is performed on cantilever beam, and the dynamic characteristics are analyzed by NExT and ERA. The results of such experiments are compared to those of FE analysis, and this comparison enables us to overcome some errors in experiments and analysis. On the basis of improved results by the comparison, model updating is performed in order to construct a basic structure for health monitoring. For model updating, we employ direct matrix updating method (DMUM) and Error matrix method (EMM) in which ambient vibration is easily applied. The model updating by the methods are again evaluated in terms of error ratio of natural frequency, comparing each result before and after updating. Finally, we perform experiments on damage detection to verify the method of updating presented here, and evaluate its performance by eigen-parameter change method. The evaluation proves that the method of FE model updating using ambient vibration is effective for health monitoring of structure, and some further application of this method is suggested.

Extending Blind Modal Identification to the Underdetermined Case for Ambient Vibration

2012 ◽  
Author(s):  
Scot McNeill
Keyword(s):  
Natural Frequencies ◽  
Second Order ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Frequency Domain Method ◽  
Blind Identification ◽  
Vibration Data ◽  
Modal Damping ◽  
Six Dof

The modal identification framework known as Blind Modal Identification (BMID) has recently been developed, drawing on techniques from Blind Source Separation (BSS). Therein, a BSS algorithm known as Second Order Blind Identification (SOBI) was adapted to solve the Modal IDentification (MID) problem. One of the drawbacks of the technique is that the number of modes identified must be less than the number of sensors used to measure the vibration of the equipment or structure. In this paper, an extension of the BMID method is presented for the underdetermined case, where the number of sensors is less than the number of modes to be identified. The analytic signal formed from measured vibration data is formed and the Second Order Blind Identification of Underdetermined Mixtures (SOBIUM) algorithm is applied to estimate the complex-valued modes and modal response autocorrelation functions. The natural frequencies and modal damping ratios are then estimated from the corresponding modal auto spectral density functions using a simple Single Degree Of Freedom (SDOF), frequency-domain method. Theoretical limitations on the number of modes identified given the number of sensors are provided. The method is demonstrated using a simulated six DOF mass-spring-dashpot system excited by white noise, where displacement at four of the six DOF is measured. All six modes are successfully identified using data from only four sensors. The method is also applied to a more realistic simulation of ambient building vibration. Seven modes in the bandwidth of interest are successfully identified using acceleration data from only five DOF. In both examples, the identified modal parameters (natural frequencies, mode shapes, modal damping ratios) are compared to the analytical parameters and are demonstrated to be of good quality.

scholarly journals DETERMINATION OF THE EFFECT OF TiO2 ON THE DYNAMIC BEHAVIOR OF SCALED CONCRETE CHIMNEY BY OMA

2021 ◽  
Vol 55 (3) ◽  
Author(s):  
Sertaç Tuhta ◽  
Furkan Günday
Keyword(s):  
Titanium Dioxide ◽  
Outer Surface ◽  
Ground Level ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Dynamic Parameters ◽  
Vibration Data ◽  
Output Only ◽  
Frequency Domain Decomposition

In this article, the dynamic parameters (frequencies, mode shapes, damping ratios) of a scaled concrete chimney and the dynamic parameters (frequencies, mode shapes, damping ratios) of the entire outer surface of the 80-micron-thick titanium dioxide are compared using the operational modal analysis method. Ambient excitation was provided from micro tremor ambient vibration data at ground level. Enhanced Frequency Domain Decomposition (EFDD) is used for the output-only modal identification. From this study, very best correlation is found between the mode shapes. Titanium dioxide applied to the entire outer surface of the scaled concrete chimney has an average of 16.34 % difference in frequency values and 9.81 % in damping ratios, proving that nanomaterials can be used to increase the rigidity in chimneys, in other words, for reinforcement. Another important result determined in the study is that it has been observed that the adherence of titanium dioxide and similar nanomaterials mentioned in the introduction to concrete chimney surfaces is at the highest level.

Structural Identification and Numerical Models for Slender Historical Structures

2015 ◽  
pp. 674-703 ◽  
Author(s):  
Dora Foti ◽  
Mariella Diaferio ◽  
Nicola Ivan Giannoccaro ◽  
Salvador Ivorra
Keyword(s):  
Model Updating ◽  
Numerical Models ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Fe Model ◽  
Detailed Structural Analysis ◽  
Ambient Vibration Tests ◽  
Great Confidence ◽  
Vibration Tests ◽  
The Time Domain

In the present chapter the theoretical basis of different methods developed for the calibration of FEMs are discussed. In general, Model Updating techniques are based on the use of appropriate functions that iteratively update selected physical properties (characteristics of the materials, stiffness of a link, etc.). In this way the correlation between the simulated response and the target value could improve if compared to an initial value. The FE model thus obtained can be used for a detailed structural analysis with a great confidence. The technique described in the first part of the chapter is applied to the evaluation of the structural properties of the tower of the Provincial Administration Building in Bari (Italy).The final purpose is to predict the performance of the tower to different combinations of static and dynamic loads, i.e. earthquakes or other induced vibrations. Ambient vibration tests have been performed on the above mentioned tower with the aim of determining its dynamic response and developing a procedure for modeling this building (Foti et al., 2012a). The Operation Modal Analysis (OMA) has been carried out both in the frequency domain and in the time domain to extract the dominant frequencies and mode shapes of the tower.

scholarly journals Identification, Model Updating, and Response Prediction of an Instrumented 15-Story Steel-Frame Building

2006 ◽  
Vol 22 (3) ◽  
pp. 781-802 ◽  
Author(s):  
Derek Skolnik ◽  
Ying Lei ◽  
Eunjong Yu ◽  
John W. Wallace
Keyword(s):  
Structural Damage ◽  
Model Updating ◽  
Three Dimensional ◽  
Response Prediction ◽  
Element Model ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Northridge Earthquake ◽  
Vibration Data ◽  
Modal Properties

Identification of the modal properties of the UCLA Factor Building, a 15-story steel moment-resisting frame, is performed using low-amplitude earthquake and ambient vibration data. The numerical algorithm for subspace state-space system identification is employed to identify the structural frequencies, damping ratios, and mode shapes corresponding to the first nine modes. The frequencies and mode shapes identified based on the data recorded during the 2004 Parkfield earthquake ( Mw=6.0) are used to update a three-dimensional finite element model of the building to improve correlation between analytical and identified modal properties and responses. A linear dynamic analysis of the updated model excited by the 1994 Northridge earthquake is performed to assess the likelihood of structural damage.

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