A Sensitivity-Enhancing Control Approach for Structural Model Updating

2007 ◽  
Author(s):  
L. J. Jiang ◽  
K. W. Wang ◽  
J. Tang
Keyword(s):  
Closed Loop ◽  
Structural Model ◽  
Natural Frequencies ◽  
Model Updating ◽  
Measurement Data ◽  
Frequency Measurement ◽  
Physical Parameters ◽  
Fe Model ◽  
Initial Model ◽  
Closed Loop Systems

Model updating plays an important role in structural design and dynamic analysis. The process of model updating aims to produce an improved mathematical model by correlating the initial model with the experimentally measured data. There are a variety of techniques available for model updating using dynamic and static measurements of the structure’s behavior. This paper focuses on the model updating methods using the measured natural frequencies of the structure. The practice of model updating using only the natural frequencies encounters two well-known limitations: deficiency of frequency measurement data, and low sensitivity of measured natural frequencies with respect to the physical parameters that need to be updated. To overcome these limitations, a novel model updating method is presented in this paper. First, closed-loop control is applied to the structure to enhance the sensitivity of natural frequencies to the updating parameters. Second, by including the natural frequencies based on a series of sensitivity-enhanced closed-loop systems, we can significantly enrich the frequency measurement data available for model updating. Using the natural frequencies of these sensitivity-enhanced closed-loop systems, an iterative process is utilized to update the physical parameters in the initial model. To demonstrate and verify the proposed method, case studies are carried out using a cantilevered beam structure. The natural frequencies of a series of sensitivity-enhanced closed-loop systems are utilized to update the mass and stiffness parameters in the initial FE model. Results show that the modeling errors in the mass and stiffness parameters can be accurately identified by using the proposed model updating method.

scholarly journals Comparative Modal Analysis of Monopile and Jacket Supported Offshore Wind Turbines including Soil-Structure Interaction

2020 ◽  
Vol 20 (10) ◽  
pp. 2042016
Author(s):  
A. Abdullahi ◽  
Y. Wang ◽  
S. Bhattacharya
Keyword(s):  
Wind Turbines ◽  
Soil Structure ◽  
Natural Frequencies ◽  
Model Updating ◽  
Offshore Wind ◽  
Fe Model ◽  
Analysis And Design ◽  
Offshore Wind Turbines ◽  
Future Design ◽  
Wide Range

Offshore wind turbines (OWTs) have emerged as a reliable source of renewable energy, witnessing massive deployment across the world. While there is a wide range of support foundations for these structures, the monopile and jacket are most utilized so far; their deployment is largely informed by water depths and turbine ratings. However, the recommended water depth ranges are often violated, leading to cross-deployment of the two foundation types. This study first investigates the dynamic implication of this practice to incorporate the findings into future analysis and design of these structures. Detailed finite element (FE) models of Monopile and Jacket supported OWTs are developed in the commercial software, ANSYS. Nonlinear soil springs are used to simulate the soil-structure interactions (SSI) and the group effects of the jacket piles are considered by using the relevant modification factors. Modal analyzes of the fixed and flexible-base cases are carried out, and natural frequencies are chosen as the comparison parameters throughout the study. Second, this study constructs a few-parameters SSI model for the two FE models developed above, which aims to use fewer variables in the FE model updating process without compromising its simulation quality. Maximum lateral soil resistance and soil depths are related using polynomial equations, this replaces the standard nonlinear soil spring model. The numerical results show that for the same turbine rating and total height, jacket supported OWTs generally have higher first-order natural frequencies than the monopile supported OWTs, while the reverse is true for the second-order vibration modes, for both fixed and flexible foundations. This contributes to future design considerations of OWTs. On the other hand, with only two parameters, the proposed SSI model has achieved the same accuracy as that using the standard model with seven parameters. It has the potential to become a new SSI model, especially for the identification of soil properties through the model updating process.

Comparing of Direct and Sensitivity-Base Model Updating Methods in Structural Dynamics and Its Application for Updating of Cantilever Model

2008 ◽  
Author(s):  
K. Abasi ◽  
M. Asayesh ◽  
M. Nikravesh
Keyword(s):  
Structural Dynamics ◽  
Model Updating ◽  
A Priori ◽  
Physical Parameters ◽  
Fe Model ◽  
Structural Components ◽  
Modal Data ◽  
Fe Model Updating ◽  
Fixed Boundary Condition ◽  
Small Rotation

Reliable finite element (FE) modeling in structural dynamics is very important for studies related to the safety of structural components used in industry. FE model updating is a tool to produce these reliable models. The method uses an initial FE model and experimental modal data of the structural components to modify physical parameters of the initial FE model, and a number of approaches have been developed to perform this task. This paper presents an overview of model updating and particularly its application for updating of cantilever model. An example of the need for model updating is a cantilever beam, where often the beam is assumed to be rigidly fixed at the clamped end. However, during tests it is often found that the beam has either a small rotation or deflection at the clamped end. If one has to construct the FE model without the knowledge of the experimental modal data, the natural assumption would be to include an ideal, fixed boundary condition, which may not be true. Even with such a simple structure the FE model is not reliable a priori, and based on intuition or engineering judgments it is difficult to estimate the values of the boundary stiffnesses. However, after creating an initial FE model, the model should be updated based on the experimental modal data obtained from modal tests so that the FE model may be used with confidence for further analysis.

scholarly journals Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios

2021 ◽  
Vol 11 (22) ◽  
pp. 10691
Author(s):  
Rúben Silva ◽  
Diogo Ribeiro ◽  
Cássio Bragança ◽  
Cristina Costa ◽  
António Arêde ◽  
...  
Keyword(s):  
Natural Frequencies ◽  
Model Updating ◽  
Numerical Models ◽  
Vertical Direction ◽  
Modal Identification ◽  
Mode Shapes ◽  
Elastic Behaviour ◽  
Test Duration ◽  
Fe Model ◽  
Dynamic Tests

This article presents an efficient methodology for the calibration of a numerical model of a Sgnss freight railway wagon based on experimental modal parameters, namely natural frequencies and mode shapes. Dynamic tests were performed for two distinct static loading configurations, tare weight and current operational overload, under demanding test conditions, particularly during an unloading operation of the train and without disturbing its tight operational schedule. These conditions impose restrictions to the tests, especially regarding the test duration, sensor positioning and system excitation. The experimental setups involve the use of several high-sensitivity accelerometers strategically distributed along with the vehicle platform and bogies in the vertical direction. The modal identification was performed with the application of the enhanced frequency-domain decomposition (EFDD) method, allowing the estimation of 10 natural frequencies and mode shapes associated with structural movements of the wagon platform, which in some cases are coupled with rigid body movements. A detailed 3D FE model of the freight wagon was developed including the platform, bogies, wheelsets, primary suspensions and wheel–rail interface. The model calibration was performed sequentially, first with the unloaded wagon model and then with the loaded wagon model, resorting to an iterative method based on a genetic algorithm. The calibration process allowed the obtainment of the optimal values of eight numerical parameters, including a double estimation of the vertical stiffness of the primary suspensions under the unloaded and loaded static configurations. The results demonstrate that the primary suspensions present an elastic/almost elastic behaviour. The comparison of experimental and numerical responses before and after calibration revealed significant improvements in the numerical models and a very good correlation between the experimental and numerical responses after calibration.

scholarly journals Damage detection of beam by natural frequencies: General theory and procedure

2006 ◽  
Vol 28 (2) ◽  
pp. 120-132 ◽  
Author(s):  
Nguyen Tien Khiem
Keyword(s):  
Boundary Conditions ◽  
Damage Detection ◽  
Natural Frequencies ◽  
Model Updating ◽  
Measurement Data ◽  
Main Tool ◽  
Frequency Equation ◽  
Geometrical Parameters ◽  
Arbitrary Boundary ◽  
Updating Procedure

The frequency equation of single damaged beam has been established for arbitrary boundary conditions that is the main tool for analysis as well as identification of damaged beam by using measured natural frequencies. A procedure for damage detection problem presented in this paper consists of three steps. First, the modelling error is reduced by a model updating procedure, in which the material, geometrical parameters and boundary conditions are updated. Then, measurement data are corrected based on the updated model. Finally, the damage parameters are identified using updated model and corrected measurement data. Theoretical investigation is illustrated by an example.

The Application of Bees Algorithm in Finite Element Model Updating

2010 ◽  
Author(s):  
Laleh Fatahi ◽  
Shapour Moradi ◽  
Pejman Razi
Keyword(s):  
Finite Element ◽  
Objective Function ◽  
Model Updating ◽  
Research Work ◽  
Modal Testing ◽  
Bees Algorithm ◽  
Physical Parameters ◽  
Piping System ◽  
Fe Model ◽  
Fe Model Updating

This research work is aimed to investigate the application of bees algorithm (BA) to the finite element (FE) model updating. BA is an evolutionary optimization algorithm that imitates the natural foraging behavior of the honeybees to find the global optimum of an objective function. Here, the weighted squared sum of the error between the measured modal parameters and the FE model predictions is considered as the objective function. To demonstrate the effectiveness of the proposed method, BA is applied on a piping system to update several physical parameters of its FE model. The results obtained from the numerical model are compared with the experimental ones obtained through the modal testing. The results show that BA successfully updates the FE model. Moreover, the performance of this approach is compared with two popular optimization methods; the genetic algorithm (GA) and the particle swarm optimization (PSO). The comparison shows the advantage of BA over GA and its similarity to PSO in terms of accuracy in the presented case study. However, BA reaches to the optimum solution faster than PSO and GA. Therefore, it can be concluded that BA is a robust and accurate optimization method that could be a good candidate for the FE model updating.

scholarly journals Bridge Scour Identification and Field Application Based on Ambient Vibration Measurements of Superstructures

2019 ◽  
Vol 7 (5) ◽  
pp. 121 ◽  
Author(s):  
Wen Xiong ◽  
C.S. Cai ◽  
Bo Kong ◽  
Xuefeng Zhang ◽  
Pingbo Tang
Keyword(s):  
Natural Frequencies ◽  
Model Updating ◽  
Ambient Vibration ◽  
Hangzhou Bay ◽  
Scour Depth ◽  
Fe Model ◽  
Bridge Scour ◽  
Vibration Measurements ◽  
Ambient Vibration Measurements ◽  
Best Fit

A scour identification method was developed based on the ambient vibration measurements of superstructures. The Hangzhou Bay Bridge, a cable-stayed bridge with high scour potential, was selected to illustrate the application of this method. Firstly, two ambient vibration measurements were conducted in 2013 and 2016 by installing the acceleration sensors on the girders and pylon. By modal analysis, the natural frequencies of the superstructures were calculated with respect to different mode shapes. Then, by tracing the change of dynamic features between two measurements in 2013 and 2016, the discrepancies of the support boundary conditions, i.e., at the foundation of the Hangzhou Bay Bridge, were detected, which, in turn, qualitatively identified the existence of bridge foundation scour. Secondly, an FE model of the bridge considering soil-pile interaction was established to further quantify the scour depth in two steps. (1) The stiffness of the soil springs representing the support boundary of the bridge was initially identified by the model updating method. In this step, the principle for a successful identification is to make the simulation results best fit the measured natural frequencies of those modes insensitive to the scour. (2) Then, using the updated FE model, the scour depth was identified by updating the depth of supporting soils. In this step, the principle of model updating is to make the simulation results best fit the measured natural frequency changes of those modes sensitive to the scour. Finally, a comparison to the underwater terrain map of the Hangzhou Bay Bridge was carried out to verify the accuracy of the predicted scour depth. Based on the study in this paper, it shows that the proposed method for identifying bridge scour based on the ambient vibration measurements of superstructures is effective and convenient. It is feasible to quickly assess scour conditions for a large number of bridges without underwater devices and operations.

scholarly journals Detection of Earthquake-Induced Damage in a Framed Structure Using a Finite Element Model Updating Procedure

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Eunjong Yu ◽  
Seung-Nam Kim ◽  
Taewon Park ◽  
Sang-Hyun Lee
Keyword(s):  
Finite Element ◽  
Natural Frequencies ◽  
Model Updating ◽  
Element Model ◽  
Fe Model ◽  
Strong Earthquakes ◽  
Frame Element ◽  
Fe Model Updating ◽  
Observed Damage ◽  
Updating Procedure

Damage of a 5-story framed structure was identified from two types of measured data, which are frequency response functions (FRF) and natural frequencies, using a finite element (FE) model updating procedure. In this study, a procedure to determine the appropriate weightings for different groups of observations was proposed. In addition, a modified frame element which included rotational springs was used to construct the FE model for updating to represent concentrated damage at the member ends (a formulation for plastic hinges in framed structures subjected to strong earthquakes). The results of the model updating and subsequent damage detection when the rotational springs (RS model) were used were compared with those obtained using the conventional frame elements (FS model). Comparisons indicated that the RS model gave more accurate results than the FS model. That is, the errors in the natural frequencies of the updated models were smaller, and the identified damage showed clearer distinctions between damaged and undamaged members and was more consistent with observed damage.

scholarly journals Substructure Model Updating Through Iterative Convex Optimization

2012 ◽  
Author(s):  
Dapeng Zhu ◽  
Yang Wang
Keyword(s):  
Convex Optimization ◽  
Structural Model ◽  
Model Updating ◽  
Dominant Mode ◽  
Mode Shapes ◽  
Fe Model ◽  
Mass Model ◽  
Actual Structure ◽  
Residual Structure ◽  
Fe Model Updating

In order to obtain a more accurate finite element (FE) model for a built structure, experimental data collected from the actual structure can be used to update the FE model. This process is known as FE model updating. Numerous FE model updating algorithms have been developed in the past few decades. However, most existing algorithms suffer computational challenges, particularly when applied to a large structure with dense measurements. The reason is these approaches usually operate on a relatively complicated model for the entire structure. To address this issue, a substructure updating approach is presented in this paper. The Craig-Bampton theory is adopted to condense the entire structural model into a substructure (currently being analyzed) and a residual structure. Dynamic response of the residual structure is approximated using only a limited number of dominant mode shapes. To improve the convergence of this substructure approach for model updating, an iterative convex optimization procedure is developed and validated through numerical simulation with a 200 degrees-of-freedom spring-mass model. The proposed substructure model updating is shown to successfully detect the locations and severities of simulated damage.

FE Model Updating for Damage Detection – Application to a Welded Structure

2009 ◽  
Vol 413-414 ◽  
pp. 393-400 ◽  
Author(s):  
Nurulakmar Abu Husain ◽  
Andy Snaylam ◽  
Hamed Haddad Khodaparast ◽  
S. James ◽  
Geoff Dearden ◽  
...  
Keyword(s):  
Structural Damage ◽  
Model Updating ◽  
Physical Parameters ◽  
Model Parameters ◽  
Fe Model ◽  
Automotive Body ◽  
Modal Properties ◽  
Numerical Predictions ◽  
Welded Structure ◽  
Fe Model Updating

Finite Element (FE) model updating is initially developed to update numerical models of structures to match their experimentally measured modal properties (i.e., natural frequencies and modes). In FE model updating, uncertain physical parameters of a structure are modified so that the discrepancies between the numerically estimated and experimentally measured modal properties are minimized. The process of updating is employed not only in parameter identification; it can also be developed for structural damage identification. In this work, a welded structure that is intended to represent a common configuration used in automotive body construction is investigated. It is known that presence of any damage in the welds of such a structure could affect its dynamic behavior. So, in theory modal test data can allow damage to be assessed accurately. As a typical automotive body contains thousands of welds, the effects of damage in the welds could be influential. The FE model updating process using experimental data is presented. It is carried out using NASTRAN optimization code. The procedure aims to adjust the uncertain properties of the FE model (from the weld joints) by minimizing the differences between the measured modal properties and the corresponding numerical predictions. The initial parameter values used in the numerical model are the nominal values. The procedure brings the numerical results of the structure as close as possible to the experimental ones, according to an objective function, therefore altering some of the FE model parameters of the structure. It may be concluded that when the identified values of certain parameters deviates from the nominal values to certain extent, there is a fault or damage at that particular joint.

scholarly journals Bayesian Finite Element Model Updating and Assessment of Cable-Stayed Bridges Using Wireless Sensor Data

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3057 ◽  
Author(s):  
Parisa Asadollahi ◽  
Yong Huang ◽  
Jian Li
Keyword(s):  
Bayesian Inference ◽  
Prediction Error ◽  
Structural Model ◽  
Model Updating ◽  
Real Data ◽  
Model Parameters ◽  
Fe Model ◽  
Inference Method ◽  
Bayesian Inference Method ◽  
Fe Model Updating

We focus on a Bayesian inference framework for finite element (FE) model updating of a long-span cable-stayed bridge using long-term monitoring data collected from a wireless sensor network (WSN). A robust Bayesian inference method is proposed which marginalizes the prediction-error precisions and applies Transitional Markov Chain Monte Carlo (TMCMC) algorithm. The proposed marginalizing error precision is compared with other two treatments of prediction-error precisions, including the constant error precisions and updating error precisions through theoretical analysis and numerical investigation based on a bridge FE model. TMCMC is employed to draw samples from the posterior probability density function (PDF) of the structural model parameters and the uncertain prediction-error precision parameters if required. It is found that the proposed Bayesian inference method with prediction-error precisions marginalized as “nuisance” parameters produces an FE model with more accurate posterior uncertainty quantification and robust modal property prediction. When applying the identified modal parameters from acceleration data collected during a one-year period from the large-scale WSN on the bridge, we choose two candidate model classes using different parameter grouping based on the clustering results from a sensitivity analysis and apply Bayes’ Theorem at the model class level. By implementing the TMCMC sampler, both the posterior distributions of the structural model parameters and the plausibility of the two model classes are characterized given the real data. Computation of the posterior probabilities over the candidate model classes provides a procedure for Bayesian model class assessment, where the computation automatically implements Bayesian Ockham razor that trades off between data-fitting and model complexity, which penalizes model classes that “over-fit” the data. The results of FE model updating and assessment based on the real data using the proposed method show that the updated FE model can successfully predict modal properties of the structural system with high accuracy.

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