Bayesian model updating of a twin-tower masonry structure through subset simulation optimization using ambient vibration data

2020 ◽  
Author(s):  
Pei Liu ◽  
Shuqiang Huang ◽  
Mingming Song ◽  
Weiguo Yang
Keyword(s):  
Bayesian Model ◽  
Model Updating ◽  
Simulation Optimization ◽  
Masonry Structure ◽  
Ambient Vibration ◽  
Subset Simulation ◽  
Vibration Data

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 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.

Bayesian model updating of a 20-story office building utilizing operational modal analysis results

2019 ◽  
Vol 22 (16) ◽  
pp. 3385-3394
Author(s):  
Heung Fai Lam ◽  
Jun Hu ◽  
Mujib Olamide Adeagbo
Keyword(s):  
Bayesian Model ◽  
Structural Model ◽  
Model Updating ◽  
Dynamic Properties ◽  
Ambient Vibration ◽  
Modal Parameters ◽  
Vibration Test ◽  
Ambient Vibration Test ◽  
Building Model ◽  
Shear Building

Most existing buildings are not equipped with long-term monitoring system. For the structural model updating and damage detection of this type of structures, ambient vibration test is popular as artificial excitation is not required. This article presents in detail the full-scale ambient vibration test, operational modal analysis, and model updating of a tall building. To capture the dynamic properties of the target 20-story building with limited number of sensors, a 15-setup ambient vibration test was designed to cover at least three measurement points (each consists of a vertical and two orthogonal horizontal measured degrees of freedom) for each selected floor. The modal parameters of each setup were extracted from the measured acceleration signals using a frequency domain decomposition method and were combined to form the global mode shape through the least-squares method. Due to the regularity of the building, a simple class of shear building models was employed to capture the dynamic characteristics of the building under lateral vibration. The identified modal parameters of the building were employed for the model updating of the shear building model to identify the distribution of inter-story stiffness. Since the “amount” of the measured information is small when compared to the “amount” of required information for identifying the uncertain parameters, the model updating problem is unidentifiable. To handle this problem, the Markov chain Monte Carlo–based Bayesian model updating method is employed in this study. The identified modal parameters revealed interesting features about the dynamic properties of the building. The well-matched results between model-predicted and identified modal parameters show the validity of the shear building model in this case study. This study provides valuable experience in the area of structural model updating and structural health monitoring.

scholarly journals Effects of variability in ambient vibration data on model updating and damage identification of a 10-story building

2017 ◽  
Vol 151 ◽  
pp. 540-553 ◽  
Author(s):  
Amin Nozari ◽  
Iman Behmanesh ◽  
Seyedsina Yousefianmoghadam ◽  
Babak Moaveni ◽  
Andreas Stavridis
Keyword(s):  
Damage Identification ◽  
Model Updating ◽  
Ambient Vibration ◽  
Vibration Data ◽  
Story Building

scholarly journals Model updating of real structures with ambient vibration data

2016 ◽  
Vol 6 (3) ◽  
pp. 329-341 ◽  
Author(s):  
K. A. T. L. Kodikara ◽  
T. H. T. Chan ◽  
T. Nguyen ◽  
D. P. Thambiratnam
Keyword(s):  
Model Updating ◽  
Ambient Vibration ◽  
Vibration Data

Identification of Structural Stiffness and Mass using Bayesian Model Updating Approach with Known Added Mass: Numerical Investigation

2020 ◽  
Vol 20 (11) ◽  
pp. 2050123
Author(s):  
Jice Zeng ◽  
Young Hoon Kim
Keyword(s):  
Bayesian Approach ◽  
Bayesian Model ◽  
Prior Probability ◽  
Model Updating ◽  
Coupling Effect ◽  
Structural Parameters ◽  
Original System ◽  
New Approach ◽  
Vibration Data ◽  
Structural Mass

The Bayesian model updating approach (BMUA) has been widely used to update structural parameters using modal measurements because of its powerful ability to handle uncertainties and incomplete data. However, a conventional BMUA is mainly used to update stiffness with the assumption that structural mass is known. Because simultaneously updating stiffness and mass leads to unidentifiable case or coupling effect of stiffness and mass, this assumption in conventional BMUA is questionable to update stiffness when the mass has significantly changed. This study proposes a new updating framework based on two structural systems: original and modified systems. A modified system is created by adding known mass to the original system. Different from the conventional Bayesian approach, two sets of measured vibration data in the proposed Bayesian approach are obtainable to address the coupling effect existing in the conventional Bayesian approach. To this end, a new approach reformulates the prior probability distribution function and the objective function. Two numerical simulations are considered to demonstrate the performance of the proposed approach, including (1) parameter identification, (2) posterior uncertainties, (3) probabilistic damage detections. The proposed BMUA outperforms a conventional BMUA in identifying both stiffness and mass.

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