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.

Effects of full-height nonstructural partitions on modal properties of two nominally identical building floors

2009 ◽  
Vol 36 (7) ◽  
pp. 1121-1132 ◽  
Author(s):  
Z. Miskovic ◽  
A. Pavic ◽  
P. Reynolds
Keyword(s):  
Model Updating ◽  
Modal Testing ◽  
The Other ◽  
Fe Model ◽  
Nonstructural Components ◽  
Modal Properties ◽  
Open Plan ◽  
Fe Model Updating ◽  
Updating Procedure ◽  
Full Height

This paper presents a combined experimental and numerical investigation of the modal properties of two full-scale and nominally identical steel–concrete composite floors. The floors were one above the other in the same fully operational multi-storey building. Both floors accommodated open-plan as well as partitioned offices. Multi-input-multi-output (MIMO) modal testing was employed to measure as-built modal properties of both floors. It was found that the two nominally identical floors had different modal characteristics, likely due to the different arrangement of partitions in the floor. It was also found that the measured modes on both floor levels experienced a considerable level of complexity, likely to be caused by nonproportional damping. Finite element (FE) models were developed in ANSYS for both floors using best engineering judgement and their features and properties were then tuned to match the measured counterparts. The tuning was done manually by trial-and-error and then automatically using sensitivity-based FE model updating procedure implemented in the FEMtools software. It was found that the initial and geometrically very detailed FE models, which did not feature any nonstructural components, underestimated the measured natural frequencies by up to a considerable 20%–25%, depending on the floor level. When full-height plasterboard and glass partitions were explicitly modelled as vertical springs connected to the floor and grounded at the other end, the correlation between the experimental and FE results improved considerably.

Review and Prospect on Dynamic FE Model Updating Methods of Footbridge Structures Based on Ambient Excitation

2011 ◽  
Vol 291-294 ◽  
pp. 1572-1577
Author(s):  
Rui Zhao ◽  
Yi Gang Zhang
Keyword(s):  
Structural Damage ◽  
Optimization Design ◽  
Damage Identification ◽  
Model Updating ◽  
Target Function ◽  
Optimization Theory ◽  
Empirical Method ◽  
Fe Model ◽  
Fe Model Updating ◽  
Ambient Excitation

The discrete finite element (FE) model often cannot reflect structure characteristics accurately due to imply more idealistic assumptions and simplifications. Therefore, it is necessary to update FE model for structural damage identification, response calculation, safety evaluation, optimization design, and so on. This article will illustrate respectively three key steps of updating parameters selection, target function selection and optimization method in process of dynamic FE model updating of footbridge structures based on ambient excitation, and put forward a feasible updating method: combine empirical method with sensitivity analysis method to select updating parameters; joint natural frequencies, MAC and modal flexibility as target function; adopt optimization algorithm based on the optimization theory.

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 Finite Element Modelling and Updating of Welded Thin-Walled Beam

2018 ◽  
Vol 15 (4) ◽  
pp. 5874-5889 ◽  
Author(s):  
M. S. M. Fouzi ◽  
K. M. Jelani ◽  
N. A. Nazri ◽  
Mohd Shahrir Mohd Sani
Keyword(s):  
Finite Element ◽  
Model Updating ◽  
Normal Mode Analysis ◽  
Mode Analysis ◽  
Modal Parameters ◽  
Fe Model ◽  
Fe Modelling ◽  
Welded Structure ◽  
Thin Walled ◽  
Fe Model Updating

This article concentrates on the finite element (FE) modelling approach to model welded thin-walled beam and the adoption of model updating technique to enhance the dynamic characteristic of the FE model. Four different types of element connectors which are RBE2, CBAR, CBEAM and CELAS format are used to construct the FE model of welded structure. Normal mode analysis is performed using finite element analysis (FEA) software, MSC Patran/Nastran to extract the modal parameters (natural frequency and mode shape) of the FE model. The precision of predicted modal parameters obtained from the four models of welded structure are compared with the measured counterparts. The dynamic characteristics of a measured counterpart is obtained through experimental modal analysis (EMA) using impact hammer method with roving accelerometer under free-free boundary conditions. In correlation process, the CBAR model has been selected for updating purposes due to its accuracy in prediction with measured counterparts and contains updating parameters compared to the others. Ahead of the updating process, sensitivity analysis is made to select the most sensitive parameter for updating purpose. Optimization algorithm in MSC Nastran is used in FE model updating process. As a result, the discrepancy between EMA and FEA is managed to be reduced. It shows the percentage of error for updated CBAR model shrinks from 7.85 % to 2.07 % when compared with measured counterpart. Hence, it is found that using FE model updating process provides an efficient and systemic way to perform a feasible FE model in replicating the real structure.

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 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.

Probabilistic Finite Element Model Updating of Civil Engineering Structures: A Comparative Study

2019 ◽  
Author(s):  
Javier F. Jiménez Alonso ◽  
Emma J. Hudson ◽  
Aleksandar Pavic ◽  
Andrés Sáez
Keyword(s):  
Finite Element ◽  
Civil Engineering ◽  
Model Updating ◽  
Likelihood Method ◽  
Physical Parameters ◽  
Fe Model ◽  
Engineering Structures ◽  
Slice Sampling ◽  
Civil Engineering Structures ◽  
Fe Model Updating

<p>Finite element (FE) model updating of civil engineering structures is usually performed under the modal domain. According to this approach, the value of the main physical parameters of the structure is modified in order to reduce the relative differences between the experimental and numerical modal parameters of the structure. To date, two methods are widely used to perform the FE model updating: (i) the maximum likelihood method and (ii) the Bayesian method. The second method is usually implemented via sampling methods. Thus, the FE model updating consists in determining an efficient sampling of each considered physical parameter of the model. Herein, two sampling techniques, the Metropolis-Hastings (M-H) algorithm and the Slice Sampling (SS) algorithm, are compared when they are implemented for the FE model updating of a laboratory steel footbridge.</p>

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