Damage Identification by Computational Model Updating

2008 ◽  
Author(s):  
Michael Link ◽  
Matthias Weiland
Keyword(s):  
Experimental Data ◽  
Computational Model ◽  
Test Data ◽  
Time Domain ◽  
High Frequency ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Modal Data ◽  
Response Data

Computational model updating techniques have been developed in the past for adjusting selected parameters of large order finite element models in order to make the models compatible with experimental data. Numerical optimization procedures are applied for minimizing the differences of analytical and experimental data, for example, natural frequencies, mode shapes and/or frequency response functions. Since long these techniques have also been investigated with regard to their ability to localize and quantify structural damage. The success of such an approach is mainly governed by the quality of the damage model and its ability to describe the structural property changes due to damage in a physical meaningful way. The change of such parameters identified from test data taken continuously or temporarily over the time may serve as a feature for structural health monitoring. It is well known that low frequency vibration test data or static response data are not very well suited for detecting and quantifying localized small size damage. Exploitable results can only be expected if high spatial resolution of the response data is available. Time domain response data from impact tests carry high frequency information which usually is lost when experimental modal data are utilized for damage identification. Even so only little literature was found addressing the utilization of experimental time histories for model updating in conjunction with damage identification. The theoretical background and examples for using both types of experimental data, high resolution modal data as well as time domain data with high frequency content will be presented in the paper. These data are used in conjunction with a new technique of localizing and quantifying the damage parameters using the test data and the models of the damaged and the undamaged structure simultaneously (“multi model updating”) which is regarded as an attempt to reduce the effects of the unavoidable non-uniqueness of structural damage models.

Identification of Structural Property Degradations by Computational Model Updating

2007 ◽  
Vol 347 ◽  
pp. 19-34 ◽  
Author(s):  
Michael Link ◽  
Stefan Stöhr ◽  
Matthias Weiland
Keyword(s):  
Experimental Data ◽  
Computational Model ◽  
Structural Property ◽  
Test Data ◽  
Measurement Errors ◽  
Structural Damage ◽  
Model Updating ◽  
Moving Load ◽  
Mode Shapes ◽  
Damage Scenarios

Computational model updating techniques are used to adjust selected parameters of finite element models in order to make the models compatible with experimental data. This is done by minimizing the differences of analytical and experimental data, for example, natural frequencies and mode shapes by numerical optimization procedures. For a long time updating techniques have also been investigated with regard to their ability to localize and quantify structural damage. The success of such an approach is mainly governed by the quality of the damage model and its ability to describe the structural property changes due to damage in a physical meaningful way. Our experience has shown that due to unavoidable modelling simplifications and measurement errors the changes of the corresponding damage parameters do not always indicate structural modifications introduced by damage alone but indicate also the existence of other modelling uncertainties which may be distributed all over the structure. This means that there are two types of parameters which have to be distinguished: the damage parameters and the other parameters accounting for general modelling and test data uncertainties. Although these general parameters may be physically meaningless they are necessary to achieve a good fit of the test data and it might happen that they cannot be distinguished from the damage parameters. For complex industrial structures it is seldom possible to generate unique structural models covering all possible damage scenarios so that one has to expect, that the parameters introduced for describing the damage will not be fully consistent with the physical reality. This is the reason why in the scientific community there is still some doubt if model based techniques can be used at all for practical purposes of damage detection and quantification under in-situ environment conditions. In the present paper we summarize the methodology of computational model updating and report about our experience with damage identification exemplified by practical examples. A new technique and an application of localising and quantifying the damage from updating the parameters of the damaged and the undamaged models simultaneously using the differences of the test data from the damaged and the undamaged structure is also presented. In this application we used the deflections (influence lines) of a beam structure measured under a slowly moving load.

scholarly journals A Combined Modal Correlation Criterion for Structural Damage Identification with Noisy Modal Data

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Manolis Georgioudakis ◽  
Vagelis Plevris
Keyword(s):  
Structural Damage ◽  
High Efficiency ◽  
Damage Identification ◽  
Model Updating ◽  
Search Algorithm ◽  
Element Model ◽  
Identification Problem ◽  
Damage Scenarios ◽  
Modal Data ◽  
Structural Damage Identification

Structural damage identification is a scientific field that has attracted a lot of interest in the scientific community during the recent years. There have been many studies intending to find a reliable method to identify damage in structural elements both in location and extent. Most damage identification methods are based on the changes of dynamic characteristics and static responses, but the incompleteness of the test data is a great obstacle for both. In this paper, a structural damage identification method based on the finite element model updating is proposed, in order to provide the location and the extent of structural damage using incomplete modal data of a damaged structure. The structural damage identification problem is treated as an unconstrained optimization problem which is solved using the differential evolution search algorithm. The objective function used in the optimization process is based on a combination of two modal correlation criteria, providing a measure of consistency and correlation between estimations of mode shape vectors. The performance and robustness of the proposed approach are evaluated with two numerical examples: a simply supported concrete beam and a concrete frame under several damage scenarios. The obtained results exhibit high efficiency of the proposed approach for accurately identifying the location and extent of structural damage.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

Structural Damage Assessment Using Output-Only Measurement: Localization and Quantification

2013 ◽  
Author(s):  
Chin-Hsiung Loh ◽  
Min-Hsuan Tseng ◽  
Shu-Hsien Chao
Keyword(s):  
Damage Detection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Null Space ◽  
Singular Spectrum Analysis ◽  
Damage Index ◽  
Computation Efficiency ◽  
Output Only

One of the important issues to conduct the damage detection of a structure using vibration-based damage detection (VBDD) is not only to detect the damage but also to locate and quantify the damage. In this paper a systematic way of damage assessment, including identification of damage location and damage quantification, is proposed by using output-only measurement. Four level of damage identification algorithms are proposed. First, to identify the damage occurrence, null-space and subspace damage index are used. The eigenvalue difference ratio is also discussed for detecting the damage. Second, to locate the damage, the change of mode shape slope ratio and the prediction error from response using singular spectrum analysis are used. Finally, to quantify the damage the RSSI-COV algorithm is used to identify the change of dynamic characteristics together with the model updating technique, the loss of stiffness can be identified. Experimental data collected from the bridge foundation scouring in hydraulic lab was used to demonstrate the applicability of the proposed methods. The computation efficiency of each method is also discussed so as to accommodate the online damage detection.

scholarly journals Detection of Sparse Damages in Structures

2019 ◽  
Author(s):  
Natalia Sabourova ◽  
Niklas Grip ◽  
Ulf Ohlsson ◽  
Lennart Elfgren ◽  
Yongming Tu ◽  
...  
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Total Variation Regularization ◽  
Regularization Technique ◽  
Spatial Properties ◽  
Ill Posed ◽  
Pros And Cons ◽  
Kirchhoff Plate Model

<p>Structural damage is often a spatially sparse phenomenon, i.e. it occurs only in a small part of the structure. This property of damage has not been utilized in the field of structural damage identification until quite recently, when the sparsity-based regularization developed in compressed sensing problems found its application in this field.</p><p>In this paper we consider classical sensitivity-based finite element model updating combined with a regularization technique appropriate for the expected type of sparse damage. Traditionally, (I), &#119897;2- norm regularization was used to solve the ill-posed inverse problems, such as damage identification. However, using already well established, (II), &#119897;l-norm regularization or our proposed, (III), &#119897;l-norm total variation regularization and, (IV), general dictionary-based regularization allows us to find damages with special spatial properties quite precisely using much fewer measurement locations than the number of possibly damaged elements of the structure. The validity of the proposed methods is demonstrated using simulations on a Kirchhoff plate model. The pros and cons of these methods are discussed.</p>

scholarly journals Identification of Structural Damage in Bridges Using High-Frequency Vibrational Responses

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ivana Mekjavić
Keyword(s):  
High Frequency ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Damage Model ◽  
Reinforced Concrete Beam ◽  
Computational Technique ◽  
Damage Scenarios ◽  
Identification Approach ◽  
Girder Bridge

The present research aims to develop an effective and applicable structural damage detection method. A damage identification approach using only the changes of measured natural frequencies is presented. The structural damage model is assumed to be associated with a reduction of a contribution to the element stiffness matrix equivalent to a scalar reduction of the material modulus. The computational technique used to identify the damage from the measured data is described. The performance of the proposed technique on numerically simulated real concrete girder bridge is evaluated using imposed damage scenarios. To demonstrate the applicability of the proposed method by employing experimental measured natural frequencies this technique is applied for the first time to a simply supported reinforced concrete beam statically loaded incrementally to failure. The results of the damage identification procedure show that the proposed method can accurately locate the damage and predict the extent of the damage using high-frequency (here beyond the 4th order) vibrational responses.

L1 Regularized Model Updating for Structural Damage Detection

2018 ◽  
Vol 18 (12) ◽  
pp. 1850157 ◽  
Author(s):  
Yu-Han Wu ◽  
Xiao-Qing Zhou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Regularization Parameter ◽  
Structural Vibration ◽  
Mode Shapes ◽  
Modal Data ◽  
Vibration Data ◽  
Stiffness Reduction ◽  
Regularized Model

Model updating methods based on structural vibration data have been developed and applied to detecting structural damages in civil engineering. Compared with the large number of elements in the entire structure of interest, the number of damaged elements which are represented by the stiffness reduction is usually small. However, the widely used [Formula: see text] regularized model updating is unable to detect the sparse feature of the damage in a structure. In this paper, the [Formula: see text] regularized model updating based on the sparse recovery theory is developed to detect structural damage. Two different criteria are considered, namely, the frequencies and the combination of frequencies and mode shapes. In addition, a one-step model updating approach is used in which the measured modal data before and after the occurrence of damage will be compared directly and an accurate analytical model is not needed. A selection method for the [Formula: see text] regularization parameter is also developed. An experimental cantilever beam is used to demonstrate the effectiveness of the proposed method. The results show that the [Formula: see text] regularization approach can be successfully used to detect the sparse damaged elements using the first six modal data, whereas the [Formula: see text] counterpart cannot. The influence of the measurement quantity on the damage detection results is also studied.

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