Structural Damage Detection Based on an Improved Edge-Detection Technique

2013 ◽  
Author(s):  
Peng Chen ◽  
Guangda Hu ◽  
Soheil Nazarian ◽  
Guirong Yan
Keyword(s):  
Edge Detection ◽  
Damage Detection ◽  
Structural Damage ◽  
Computational Effort ◽  
Mode Shapes ◽  
Jump Discontinuities ◽  
Bridge Model ◽  
Polynomial Annihilation ◽  
Different Types ◽  
Edge Detection Method

To localize small damage from mode shapes, the polynomial annihilation edge detection method has been proposed and demonstrated its effectiveness on different types of structural components [7]. However, much computational effort involved in this approach lowers the damage detection speed. To alleviate this difficulty, in this paper, we improve the approach by first using the divided difference approach to identify the region(s) in which jump discontinuities are located, and then only applying the polynomial annihilation method to points in the identified region. In this way, the computational burden of this approach is significantly relieved, while the accuracy is still maintained. The improved approach has been validated by numerical simulations on a cable-stayed bridge model. This approach only requires post-damage mode shapes.

Detection and Localisation of Structural Damage Based on the Polynomial Annihilation Edge Detection: An Experimental Verification

2013 ◽  
Vol 569-570 ◽  
pp. 1273-1280 ◽  
Author(s):  
Cecilia Surace ◽  
Massimiliano Mattone ◽  
Marco Gherlone
Keyword(s):  
Edge Detection ◽  
Damage Detection ◽  
Structural Damage ◽  
High Spatial Resolution ◽  
Mode Shapes ◽  
Laser Vibrometer ◽  
Numerical Assessment ◽  
Polynomial Annihilation ◽  
Vibration Tests ◽  
Derivatives Of

The present paper describes an experimental validation of a new structural damage detection method based on the Polynomial Annihilation Edge Detection (PAED) technique. It is well known that concentrated damage such as a crack, causes a discontinuity in the rotations and consequently in the first derivatives of the mode shapes. On this basis, the PAED, a numerical method for detecting discontinuities in smooth piecewise functions and their derivatives, can be applied to the problem of damage detection and localisation in beam-like structures for which only post-damage mode shapes are available. As described in this paper, in order to verify this approach experimentally (a numerical assessment having already been documented in previous papers), vibration tests on a cantilever steel beam with a saw-cut have been performed and the Operational Deflection Shapes (ODS) determined. As the approach requires a reasonably high spatial resolution of the ODS, a scanning laser vibrometer, capable of acquiring data rapidly at a very large number of observation points, was used.

scholarly journals Dynamic Identification Techniques to Numerically Detect the Structural Damage

2013 ◽  
Vol 7 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Dora Foti
Keyword(s):  
Finite Element ◽  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Element Model ◽  
Mode Shapes ◽  
Dynamic Identification ◽  
Engineering Structures ◽  
Bridge Model ◽  
Modal Curvature

Damage detection in civil engineering structures using changes in measured modal parameters is an area of research that has received notable attention in literature in recent years. In this paper two different experimental techniques for predicting damage location and severity have been considered: the Change in Mode Shapes Method and the Mode Shapes Curvature Method. The techniques have been applied to a simply supported finite element bridge model in which damage is simulated by reducing opportunely the flexural stiffness EI. The results show that a change in modal curvature is a significant damage indicator, while indexes like MAC and COMAC – extensively and correctly used for finite element model updating - lose their usefulness in order to damage detection.

FEM Free Damage Detection of Beam Structures Using the Deflections Estimated by Modal Flexibility Matrix

2021 ◽  
pp. 2150128
Author(s):  
Wen-Yu He ◽  
Wei-Xin Ren ◽  
Lei Cao ◽  
Quan Wang
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Index ◽  
Element Model ◽  
Mode Shapes ◽  
Beam Structures ◽  
Flexibility Matrix ◽  
Damage Quantification ◽  
Modal Flexibility ◽  
The Cost

The deflection of the beam estimated from modal flexibility matrix (MFM) indirectly is used in structural damage detection due to the fact that deflection is less sensitive to experimental noise than the element in MFM. However, the requirement for mass-normalized mode shapes (MMSs) with a high spatial resolution and the difficulty in damage quantification restricts the practicability of MFM-based deflection damage detection. A damage detection method using the deflections estimated from MFM is proposed for beam structures. The MMSs of beams are identified by using a parked vehicle. The MFM is then formulated to estimate the positive-bending-inspection-load (PBIL) caused deflection. The change of deflection curvature (CDC) is defined as a damage index to localize damage. The relationship between the damage severity and the deflection curvatures is further investigated and a damage quantification approach is proposed accordingly. Numerical and experimental examples indicated that the presented approach can detect damages with adequate accuracy at the cost of limited number of sensors. No finite element model (FEM) is required during the whole detection process.

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.

STUDY ON DAMAGE DETECTION OF SQUARE PYRAMID SPACE GRID STRUCTURE CONSIDERING INITIAL RANDOM DEFECTS

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Zhang Limei ◽  
Du Shoujun ◽  
Fan Meng
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Poisson Ratio ◽  
Design Method ◽  
Structural Response ◽  
Space Grid ◽  
Section Area ◽  
Different Types ◽  
Random Defects ◽  
The Ideal

Because of different types of load, material properties deviation and construction errors, structures have initial defects inevitably. Therefore structural damages emerge easily and have strong randomness. At the same time, the ideal design model often has difference with structure in service. To most structures, the initial testing dates cannot be obtained, while this initial model is very important to structural damage detection. So the ideal model needs to revise. In this paper, elastic modulus, Poisson ratio and link section area are given as initial random defects and these defects obey normal distribution which can be constructed by Monte Carlo probabilistic design method. Firstly, the sensitivity parameters to structural response will be received by PDS technology from Ansys. Next, the square pyramid space grid models with random defects were obtained. Finally, given link element damage, using the method combined curvature mode difference with wavelet transform, the link element damage can be determined. Through analysis, the effects about the initial defects to damage detection will be obtained.

scholarly journals Structural Damage Detection Using Slopes of Longitudinal Vibration Shapes

2015 ◽  
Author(s):  
W. Xu ◽  
W. D. Zhu ◽  
S. A. Smith
Keyword(s):  
Steady State ◽  
Damage Detection ◽  
Structural Damage ◽  
Longitudinal Vibration ◽  
Harmonic Excitation ◽  
Mode Shapes ◽  
Noise Robustness ◽  
Steady State Response ◽  
Structural Damage Detection ◽  
State Response

While structural damage detection based on flexural vibration shapes, such as mode shapes and steady-state response shapes under harmonic excitation, has been well developed, little attention is paid to that based on longitudinal vibration shapes that also contain damage information. This study originally formulates a slope vibration shape for damage detection in bars using longitudinal vibration shapes. To enhance noise robustness of the method, a slope vibration shape is transformed to a multiscale slope vibration shape in a multiscale domain using wavelet transform, which has explicit physical implication, high damage sensitivity, and noise robustness. These advantages are demonstrated in numerical cases of damaged bars, and results show that multiscale slope vibration shapes can be used for identifying and locating damage in a noisy environment. A three-dimensional (3D) scanning laser vibrometer is used to measure the longitudinal steady-state response shape of an aluminum bar with damage due to reduced cross-sectional dimensions under harmonic excitation, and results show that the method can successfully identify and locate the damage. Slopes of longitudinal vibration shapes are shown to be suitable for damage detection in bars and have potential for applications in noisy environments.

Frequency-based damage detection in cantilever beam using vision-based monitoring system with motion magnification technique

2018 ◽  
Vol 29 (20) ◽  
pp. 3923-3936 ◽  
Author(s):  
Andrew Jaeyong Choi ◽  
Jae-Hung Han
Keyword(s):  
Genetic Algorithm ◽  
Damage Detection ◽  
Monitoring System ◽  
Cantilever Beam ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Solution Space ◽  
Mode Shapes ◽  
High Resolution Data ◽  
Motion Magnification

This article proposes a method for damage detection using vision-based monitoring with motion magnification technique. The methods based on the vibration characteristics of structures such as natural frequency, mode shapes, and modal damping have been applied to structural damage detection. However, the conventional methods have limitations for practical applications. Vision-based monitoring system can be employed as a new structural monitoring system because of its simplicity, potentially low cost, and unique capability of collecting high-resolution data. A methodology called video motion magnification has been developed to amplify non-visible small motions in a video to reveal the dynamic response. The video motion magnification method can be applied to measure small displacements to calculate the natural frequencies and the operational deflection shapes of the structures. Unlike conventional optimization methods, a genetic algorithm explores the entire solution space and can obtain the global optimum. In this article, identification of the location and magnitude of damage in a cantilever beam is formulated as an optimization problem using a real-value genetic algorithm by minimizing the objective function, which directly compares the first three natural frequencies changes from the phase-based motion magnification measurement and from the analytical model of a damaged cantilever beam.

scholarly journals Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3371 ◽  
Author(s):  
Tao Yin ◽  
Hong-ping Zhu
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Damage Detection ◽  
Network Architecture ◽  
Structural Damage ◽  
Multiple Target ◽  
Structural Damage Detection ◽  
Bayesian Neural Network ◽  
Bridge Model ◽  
Hidden Layer

Excellent pattern matching capability makes artificial neural networks (ANNs) a very promising approach for vibration-based structural health monitoring (SHM). The proper design of the network architecture with the suitable complexity is vital to the ANN-based structural damage detection. In addition to the number of hidden neurons, the type of transfer function used in the hidden layer cannot be neglected for the ANN design. Neural network learning can be further presented in the framework of Bayesian statistics, but the issues of selection for the hidden layer transfer function with respect to the Bayesian neural network has not yet been reported in the literature. In addition, most of the research works in the literature for addressing the predictive distribution of neural network output is only for a single target variable, while multiple target variables are rarely involved. In the present paper, for the purpose of probabilistic structural damage detection, Bayesian neural networks with multiple target variables are optimally designed, and the selection of the number of neurons, and the transfer function in the hidden layer, are carried out simultaneously to achieve a neural network architecture with suitable complexity. Furthermore, the nonlinear network function can be approximately linear by assuming the posterior distribution of network parameters is a sufficiently narrow Gaussian, and then the input-dependent covariance matrix of the predictive distribution of network output can be obtained with the Gaussian assumption for the situation of multiple target variables. Structural damage detection is conducted for a steel truss bridge model to verify the proposed method through a set of numerical case studies.

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