scholarly journals Damage Identification by the Data Expansion and Substructuring Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Eun-Taik Lee ◽  
Hee-Chang Eun
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Expansion Method ◽  
External Noise ◽  
Detection Methods ◽  
Damage Indices ◽  
Before And After ◽  
Stiffness Variation ◽  
Data Expansion

Structural damage can be detected by comparing the responses before and after the damage. The responses are transformed into curvature, strain, and stress, among others, which characterize the mechanical behavior of the structural members, and can be utilized as damage indices for damage detection. The damage of a truss structure can rarely be detected by the displacements only at nodes. This work investigates damage detection methods using the stress or stiffness variation rate of the truss element before and after the damage. This paper considers three different cases according to the number of measurement locations. If the complete responses at a full set of degrees of freedom are measured, the stiffness variation rates of the elements are calculated accurately, and the damage can be explicitly detected despite external noise. If the number of measured data points is fewer than the system order, the displacements are estimated by the data expansion method, and the damage-expected regions are predicted by the stiffness variation rates. Apart from the explicitly damaged elements, the substructuring approach is adopted for closer damage detection with several measurement sensors despite external noise. It is illustrated by the examples that three cases are compared numerically. The numerical examples compare and analyze the numerical results of the three cases.

Nonlinear Structural Damage Detection Based on Adaptive Volterra Filter Model

2018 ◽  
Vol 18 (02) ◽  
pp. 1871003 ◽  
Author(s):  
J. Prawin ◽  
A. Rama Mohan Rao
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Linear Models ◽  
Time History ◽  
Diagnostic Techniques ◽  
Filter Model ◽  
Volterra Filter ◽  
Damage Indices ◽  
Before And After ◽  
Nonlinear Time History

The majority of the existing damage diagnostic techniques are based on linear models. Changes in the state of the dynamics of these models, before and after damage in the structure based on the vibration measurements, are popularly used as damage indicators. However, the system may initially behave linearly and subsequently exhibit nonlinearity due to the incipience of damage. Breathing cracks that exhibit bilinear behavior are one such example of the damage induced due to nonlinearity. Further many real world structures even in their undamaged state are nonlinear. Hence, in this paper, we present a nonlinear damage detection technique based on the adaptive Volterra filter using the nonlinear time history response. Three damage indices based on the adaptive Volterra filter are proposed and their sensitiveness to damage and noise is assessed through two numerically simulated examples. Numerical investigations demonstrate the effectiveness of the adaptive Volterra filter model to detect damage in nonlinear structures even with measurement noise.

Structural Damage Identification Based on Information Fusion Techniques

2010 ◽  
Author(s):  
Hui Li ◽  
Yuequan Bao
Keyword(s):  
Damage Detection ◽  
Information Fusion ◽  
Structural Damage ◽  
Damage Identification ◽  
Yellow River ◽  
Numerical Study ◽  
Detection Methods ◽  
Data Sets ◽  
Structural Damage Detection ◽  
Selective Fusion

With the aim to decrease the uncertainties of structural damage detection, two fusion models are presented in this paper. The first one is a weighted and selective fusion method for combing the multi-damage detection methods based on the integration of artificial neural network, Shannon entropy and Dempster-Shafer (D-S) theory. The second one is a D-S based approach for combing the damage detection results from multi-sensors data sets. Numerical study on the Binzhou Yellow River Highway Bridge and an experimental of a 20-bay rigid truss structure were carried out to validate the uncertainties decreasing ability of the proposed methods for structural damage detection. The results show that both of the methods proposed are useful to decrease the uncertainties of damage detection results.

Experimental investigation of damage identification in beam structures based on the strain statistical moment

2016 ◽  
Vol 20 (5) ◽  
pp. 747-758 ◽  
Author(s):  
Dansheng Wang ◽  
Zhen Chen ◽  
Wei Xiang ◽  
Hongping Zhu
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Gaussian White Noise ◽  
Statistical Moment ◽  
Least Square ◽  
Detection Technique ◽  
Before And After ◽  
Beam Type ◽  
Strain Responses

A new two-step damage detection technique based on the fourth strain statistical moment was recently proposed by the authors, and its sensitivity to local structural damage has been numerically demonstrated for beam-type structures. In this article, the proposed method is extended to an experimental beam to assess its feasibility and practicality. A simply supported steel beam was manufactured and subjected to Gaussian white-noise excitation before and after damage. The strain responses of each measurement point were recorded based on which fourth strain statistical moments were calculated. The proposed two-step technique was implemented to locate the damaged elements of the experimental beam, for which the damage sizes were identified based on the least-square updating algorithm. The experimental results show that the proposed fourth strain statistical moment index and the two-step damage detection technique are effective and feasible for beam-type structures.

Statistics-Based Noise Analysis for Vibration-Based Damage Identification

2006 ◽  
Author(s):  
L. Yu ◽  
T. Yin ◽  
H. P. Zhu
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Perturbation Technique ◽  
Random Perturbation ◽  
Noise Analysis ◽  
Detection Methods ◽  
Mode Shapes ◽  
Composite Function ◽  
Analysis Technique

As the vibration-based structural damage detection methods are easily affected by the environmental noise, a novel noise analysis method is proposed based on the statistics in this paper together with the Monte Carlo technique for assessing the influence of experimental noise of modal data on sensitivity-based damage detection methods. Different from the commonly used random perturbation technique, the proposed technique is deduced directly by the Moore–Penrose generalized inverse of sensitivity matrix under the differential quotient rule of composite function. It can not only make the analysis process more effective but also analyze the noise influence on both frequencies and mode shapes in a similar way. Furthermore, an improved modal sensitivity based damage detection method is also proposed and compared with other two commonly used sensitivity-based methods in this paper. A one-story portal frame is adopted to evaluate the efficiency of both the proposed noise analysis technique and the improved modal sensitivity based method. The assessment results show that the proposed statistics-based noise analysis technique is effective and more suitable for the vibration-based damage identification. The improved modal sensitivity based method is more robust to noise than the other commonly used sensitivity-based methods.

scholarly journals Comparison of Damage Detection Methods Depending on FRFs within Specified Frequency Ranges

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yong-Su Kim ◽  
Hee-Chang Eun
Keyword(s):  
Damage Detection ◽  
Resonance Frequency ◽  
Structural Damage ◽  
Detection Methods ◽  
Frequency Range ◽  
Damage Indices ◽  
Antiresonance Frequency ◽  
Limited Frequency ◽  
Proper Orthogonal ◽  
Modified Forms

Structural damage can be detected using frequency response function (FRF) measured by an impact and the corresponding responses. The change in the mechanical properties of dynamic system for damage detection can seldom be estimated using FRF data extracted from a very limited frequency range. Proper orthogonal modes (POMs) from the FRFs extracted in given frequency ranges and their modified forms can be utilized as damage indices to detect damage. The POM-based damage detection methods must be sensitive to the selected FRFs. This work compares the effectiveness of the damage detection approaches taking the POMs estimated by the FRFs within five different frequency ranges including resonance frequency and antiresonance frequency. It is shown from a numerical example that the POMs extracted from the FRFs within antiresonance frequency ranges provide more explicit information on the damage locations than the ones within resonance frequency ranges.

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 UAV-BASED STRUCTURAL DAMAGE MAPPING – RESULTS FROM 6 YEARS OF RESEARCH IN TWO EUROPEAN PROJECTS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 187-194 ◽  
Author(s):  
N. Kerle ◽  
F. Nex ◽  
D. Duarte ◽  
A. Vetrivel
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Point Cloud ◽  
Feature Fusion ◽  
Texture Features ◽  
Mobile App ◽  
Detection Methods ◽  
Support Vector ◽  
3D Point Cloud ◽  
Damage Mapping

<p><strong>Abstract.</strong> Structural disaster damage detection and characterisation is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of UAV in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.</p>

Damage Assessment of a Building Subjected to a Terrorist Attack

2014 ◽  
Vol 17 (11) ◽  
pp. 1693-1704 ◽  
Author(s):  
E.L. Eskew ◽  
S. Jang
Keyword(s):  
Damage Detection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Numerical Models ◽  
Terrorist Attack ◽  
Structural Response ◽  
Structural Condition ◽  
Condition Assessment ◽  
Detection Methods ◽  
Specific Element

An increasing threat of global terrorism has led to concerns about bombings of buildings, which could cause minor to severe structural damage. After such an event, it is important to rapidly assess the damage to the building to ensure safe and efficient emergency response. Current methods of visual inspection and non-destructive testing are expensive, subjective, and time consuming for emergency responders' usage immediately after an attack. On the other hand, vibration-based damage detection methods with wireless smart sensors could provide rapid assessment of structural characteristics with low cost. For blast analysis, structural response is usually determined using a simplified SDOF version of the undamaged structure, such as used in a Pressure-Impulse (P-I) Diagram, or using more complex FEM (finite element method) models. However, the simplified models cannot take into account damage caused by blast focus at a specific location or on a specific element, which may induce local failure leading to potential progressive collapse, and the more complex FEM models take too long to derive applicable results to be effective for a rapid structural assessment. In this paper, a new method to incorporate vibration-based damage detection methods to calculate the multi degree of freedom structural stiffness for determining structural condition is provided to create a framework for the rapid structural condition assessment of buildings after a terrorist attack. The stiffness parameters are generated from the modal analysis of the measured vibration on the building, which are then used in a numerical simulation to determine its structural response from the blast. The calculated structural response is then compared to limit conditions that have been developed from ASCE blast design codes to determine the damage assessment. A laboratory-scale building frame has been employed to validate the developed use of experimentally determined stiffness by comparing the P-I diagram using the experimental stiffness with that from numerical models. The reasonable match between the P-I diagrams from the numerical models and the experiments shows the positive potential of the method. The framework and examples of how to develop a rapid condition assessment are presented.

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