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>

Application of Bayesian statistical method in sensitivity-based seismic damage identification of structures: Numerical and experimental validation

2018 ◽  
Vol 17 (5) ◽  
pp. 1255-1276 ◽  
Author(s):  
Maryam Vahedi ◽  
Faramarz Khoshnoudian ◽  
Ting Yu Hsu
Keyword(s):  
Statistical Method ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Least Square Method ◽  
Element Model ◽  
Least Square ◽  
Detection Methods ◽  
Sensitivity Equation ◽  
Stiffness Reduction

Most of the developed sensitivity-based damage detection methods are based on the application of external excitations which could be prohibitive due to infeasible excitation of all structural degrees of freedom. In this regard, identification of damage properties using seismic structural response would be advantageous. In this research, sensitivity-based finite element model updating method is proposed to identify structural damage by earthquake response in the frequency domain and the transfer function of the structure due to ground excitation. The obtained sensitivity equation is solved by linear least square method through defining constraints on the design variables. Since the attainable measured data are restricted by limits on the instrumentations and preciseness of the measurements and due to the fact that only a few of the lower modes of a structure can generally be determined with confidence, a Bayesian statistical method is utilized to enhance the reliability of the predicted damage properties. The proposed technique is applied to a numerical frame model and an experimental six-story steel structure with various scenarios of story stiffness reduction. The results are indicative of the capability of the proposed method for identification of damage location and severity.

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.

Damage Identification Based on Power Spectral Density Sensitivity Analysis of Structural Responses

2014 ◽  
Vol 919-921 ◽  
pp. 45-50 ◽  
Author(s):  
Wei Huan Chen ◽  
Chang Ying Ding ◽  
Bing Quan He ◽  
Zhong Rong Lv ◽  
Ji Ke Liu
Keyword(s):  
Sensitivity Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Power Spectral ◽  
Structural Responses ◽  
Pseudo Excitation

A new method is proposed to identify structural damages based on the power spectral density sensitivity analysis. The responses of the structure under stationary and random excitations are obtained using pseudo excitation method, and then the sensitivities of power spectral density with respect to the structural damage parameters are obtained similarly. Finite element model updating method is adopted to identify the structural damages. A numerical example of a shearing structure demonstrates the satisfactory results obtained from the present method.

Structural Damage Identification in Vehicle-Bridge System Based on Measured Dynamic Response

2015 ◽  
Vol 764-765 ◽  
pp. 1139-1143
Author(s):  
Tai Ping Chang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Least Squares Method ◽  
Interaction Model ◽  
Element Model ◽  
Measurement Point ◽  
Sensitivity Matrix ◽  
Sensitivity Equation ◽  
Damage Indices

In the present study, a detailed vehicle–bridge dynamic interaction model is established, and the bridge is modeled as laminated composite beams which are discretized as finite beam elements. The vehicle-induced responses of the bridge in the damaged state are used as input data for damage identification and the response sensitivities with respect to the damage indices of the elements are calculated to establish the sensitivity matrix. Based on the error between the measured response and the computed one as a minimization criterion, the sensitivity equation is solved by the least-squares method, and then the damage is located and quantified with the finite element model updating technique. It can be concluded that only one measurement point is required to detect the damage of the bridge, and location of the measurement point does not significantly affect the identification result. Furthermore, it is noted that the absolute damage of any beam element is well identified by using either the displacement response, velocity response or acceleration response.

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.

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