Structural Damage Detection Using Curvature Mode Difference Curve

2011 ◽  
Vol 250-253 ◽  
pp. 1248-1251 ◽  
Author(s):  
Hang Jing ◽  
Ling Ling Jia ◽  
Yi Zhao
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Numerical Differentiation ◽  
Engineering Structures ◽  
System Parameters ◽  
Numerical Studies ◽  
Damage Indicator ◽  
Structural Damage Identification ◽  
Changing Rate

Damage detection in civil engineering structures using the dynamic system parameters has become an important area of research. The sensitivity of damage indicator is of great value to structural damage identification. The curvature mode is an excellent parameter in damage detection of structures, while in case that certain curvature mode curve can’t show existence of damage. In this paper, numerical studies are conducted to demonstrate the deficiency of curvature mode to damage detection. Then a new damage indicator called “curvature mode changing rate” (CMCR) is introduced which is processed by numerical differentiation of curvature mode curve. The simulation results show that the new index is superior to curvature mode for structural damage identification, and it is still sensitive to the damaged location in the mode node.

Review on Damage Identification and Diagnosis Research of Civil Engineering Structure

2014 ◽  
Vol 1006-1007 ◽  
pp. 34-37 ◽  
Author(s):  
Hong Ni ◽  
Ming Hui Li ◽  
Xi Zuo
Keyword(s):  
Structural Damage ◽  
Civil Engineering ◽  
Damage Identification ◽  
Engineering Structures ◽  
Engineering Structure ◽  
Civil Engineering Structure ◽  
Structure Damage ◽  
Development Direction ◽  
Structural Damage Identification ◽  
Civil Engineering Structures

This paper first describes the importance of structural damage identification and diagnosis in civil engineering, and introduces domestic and foreign status of damage identification and diagnosis methods, and on the basis of this, it also introduces all kinds of methods for damage identification and diagnosis of civil engineering structures, and finally puts forward the development direction of civil engineering structure damage identification and diagnosis.

Structural Nonlinear Damage Detection Method Using AR/ARCH Model

2017 ◽  
Vol 17 (08) ◽  
pp. 1750083 ◽  
Author(s):  
J. J. Cheng ◽  
H. Y. Guo ◽  
Y. S. Wang
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Fatigue Cracks ◽  
Arch Model ◽  
Structure Model ◽  
Relevant Research ◽  
Engineering Structures ◽  
The Past ◽  
Damage Indicator ◽  
Shear Building

Structural health monitoring (SHM) has received increasing attention in the research community over the past two decades. Most of the relevant research focuses on linear structural damage detection. However, the majority of the damage in civil engineering structures is nonlinear, such as fatigue cracks that open and close under dynamic loading. In this study, a new hybrid AR/ARCH model in the field of economics and a proposed damage indicator (DI) which is the second-order variance indicator (SOVI) based on the model have been used for detecting structural nonlinear damage. The data from an experimental three-storey structure and a simulated eight-storey shear building structure model have been used to verify the effectiveness of the algorithm and SOVI. In addition, a traditional linear DI: cepstral metric indicator (CMI) has also been used to diagnose the nonlinear damage. The results of the CMI and SOVI were compared and it is found that there are advantages in using the SOVI in the field of nonlinear structural damage.

scholarly journals Use of Bispectrum Analysis to Inspect the Non-Linear Dynamic Characteristics of Beam-Type Structures Containing a Breathing Crack

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1177
Author(s):  
Li Cui ◽  
Hao Xu ◽  
Jing Ge ◽  
Maosen Cao ◽  
Yangmin Xu ◽  
...  
Keyword(s):  
Damage Detection ◽  
Dynamic Characteristics ◽  
Structural Damage ◽  
Damage Identification ◽  
Crack Depth ◽  
Excitation Frequency ◽  
Breathing Crack ◽  
Engineering Structures ◽  
Linear Dynamic ◽  
Non Linear

A breathing crack is a typical form of structural damage attributed to long-term dynamic loads acting on engineering structures. Traditional linear damage identification methods suffer from the loss of valuable information when structural responses are essentially non-linear. To deal with this issue, bispectrum analysis is employed to study the non-linear dynamic characteristics of a beam structure containing a breathing crack, from the perspective of numerical simulation and experimental validation. A finite element model of a cantilever beam is built with contact elements to simulate a breathing crack. The effects of crack depth and location, excitation frequency and magnitude, and measurement noise on the non-linear behavior of the beam are studied systematically. The result demonstrates that bispectral analysis can effectively identify non-linear damage in different states with strong noise immunity. Compared with existing methods, the bispectral non-linear analysis can efficiently extract non-linear features of a breathing crack, and it can overcome the limitations of existing linear damage detection methods used for non-linear damage detection. This study’s outcome provides a theoretical basis and a paradigm for damage identification in cracked structures.

scholarly journals Damage Detection Based on Cross-Term Extraction from Bilinear Time-Frequency Distributions

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ma Yuchao ◽  
Yan Weiming ◽  
He Haoxiang ◽  
Wang Kai
Keyword(s):  
Damage Detection ◽  
Frequency Distribution ◽  
Structural Damage ◽  
Damage Identification ◽  
Dominant Term ◽  
Cross Term ◽  
Time Frequency ◽  
Structural Damage Identification ◽  
The Cross ◽  
Time Frequency Distribution

Abundant damage information is implicated in the bilinear time-frequency distribution of structural dynamic signals, which could provide effective support for structural damage identification. Signal time-frequency analysis methods are reviewed, and the characters of linear time-frequency distribution and bilinear time-frequency distribution typically represented by the Wigner-Ville distribution are compared. The existence of the cross-term and its application in structural damage detection are demonstrated. A method of extracting the dominant term is proposed, which combines the short-time Fourier spectrum and Wigner-Ville distribution; then two-dimensional time-frequency transformation matrix is constructed and the complete cross-term is extracted finally. The distribution character of which could be applied to the structural damage identification. Through theoretical analysis, model experiment and numerical simulation of the girder structure, the change rate of cross-term amplitude is validated to identify the damage location and degree. The effectiveness of the cross-term of bilinear time-frequency distribution for damage detection is confirmed and the analytical method of damage identification used in structural engineering is available.

scholarly journals VIBRATION-BASED STRUCTURAL DAMAGE DETECTION TECHNIQUES: A REVIEW

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Akpabot Akpabot ◽  
Anthony Ede ◽  
Oluwarotimi Olofinnade ◽  
Abimbola Odetoyan
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Structural Integrity ◽  
Damage Identification ◽  
Practical Application ◽  
Engineering Structures ◽  
Detection Techniques ◽  
The Time Domain ◽  
Stiffness And Damping ◽  
Early Damage

Damage in infrastructure can be as a result of its degenerating state under service loads or after exposure to impact loads such as earthquakes. Early damage detection is essential to preventing failure and ensure the integrity and safety of structures. Damages lead to changes in the geometric and material properties like mass, stiffness, and damping, and influences the response behavior of the structure. It has been proven that vibration-based damage detection technique is an efficient means of damage identification and assessing structural integrity. This review article examines conventional vibration-based damage detection techniques. It highlights the importance of early damage detection as a means of ensuring infrastructural safety, reliability and maintenance. Damage detection techniques like the time domain methods, frequency domain and modal domain methods have been developed and constantly evolving to meet the existing challenge of identifying structural damages. The practical application is still minimal, hence more research works are necessary for damage detection in large civil engineering structures.

scholarly journals Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network

2021 ◽  
Vol 11 (6) ◽  
pp. 2610
Author(s):  
Jongbin Won ◽  
Jong-Woong Park ◽  
Soojin Jang ◽  
Kyohoon Jin ◽  
Youngbin Kim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Input Data ◽  
Damage Identification ◽  
Beam Model ◽  
Data Normalization ◽  
Structural Damage Detection ◽  
Structural Damage Identification

In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.

Application of an Implicit Redundant Genetic Algorithm for Structural Damage Identification of Flexible Structures

2004 ◽  
Author(s):  
T. R. Liszkai
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Optimization Problems ◽  
Flexible Structures ◽  
Nuclear Industry ◽  
Superior Performance ◽  
Structural Damage Detection ◽  
Measuring Device ◽  
Structural Damage Identification

Detecting structural damage is critical in assessing current condition, calculating remaining life, and developing rehabilitation strategies for existing structures. Many structural damage identification methods (SDIM) use vibration data to localize and identify deterioration of structural members. Due to practical constraints, such as cost, number of input channels of the measuring device, or lack of access of parts of the structure, the actual number of sensors used to collect measurement data is much smaller then the number of possible sensor locations. Therefore, the inverse problem associated with structural damage identification is ill formulated and often difficult to solve explicitly. This research addresses the problem of structural damage detection using the linear vibration information contained in frequency response functions (FRF). A structural damage identification method (SDIM) is proposed, which minimizes the error between the analytically computed and measured vibration signatures of structures. The SDIM is formulated as an unconstrained optimization problem, which is solved using genetic algorithms (GA). The implicit redundant representation (IRR) of genes allows the formulation of unstructured optimization problems in which the number of unknown variables is indefinite. The IRR GA efficiently exploits the unstructured nature of structural damage detection by allowing the number of assumed damaged elements to change throughout the optimization. The accuracy and efficiency of SDIM is increased when the IRR GA is used instead of the simple fixed representation GA. The procedure is applied to flexible structures to show that the proposed SDIM is capable of identifying damages in structures often used in the nuclear industry. Noisy measurements are also considered in the simulations to investigate their effect on the proposed SDIM accuracy. Test case results using different measurement noise levels show that the IRR GA has superior performance over the standard fixed representation GA in correctly identifying both the location and extent of damages.

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