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

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.

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

Applied Sciences ◽

10.3390/app11062610 ◽

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 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.

Structural Damage Identification Based on the Minimum System Realization and Sensitivity Analysis

Mathematical Problems in Engineering ◽

10.1155/2014/405760 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12

Author(s):

W. R. Li ◽

Y. F. Du ◽

S. Y. Tang ◽

L. J. Zhao

Keyword(s):

Sensitivity Analysis ◽

Damage Detection ◽

Structural Damage ◽

Damage Identification ◽

Structural Information ◽

Structural Response ◽

Frame Structure ◽

System Realization ◽

Minimum System

On the basis of the thought that the minimum system realization plays the role as a coagulator of structural information and contains abundant information on the structure, this paper proposes a new method, which combines minimum system realization and sensitivity analysis, for structural damage detection. The structural damage detection procedure consists of three steps: (1) identifying the minimum system realization matrixes A, B, and R using the structural response data; (2) defining the mode vector, which is based on minimum system realization matrix, by introducing the concept of the measurement; (3) identifying the location and severity of the damage step by step by continuously rotating the mode vector. The proposed method was verified through a five-floor frame model. As demonstrated by numerical simulation, the proposed method based on the combination of the minimum realization system and sensitivity analysis is effective for the damage detection of frame structure. This method not only can detect the damage and quantify the damage severity, but also is not sensitive to the noise.

Structural Damage Detection Using Linear Matrix Inequality Methods

Journal of Vibration and Acoustics ◽

10.1115/1.1287029 ◽

2000 ◽

Vol 122 (4) ◽

pp. 448-455 ◽

Cited By ~ 25

Author(s):

M. O. Abdalla ◽

K. M. Grigoriadis ◽

D. C. Zimmerman

Keyword(s):

Experimental Data ◽

Linear Matrix Inequality ◽

Damage Detection ◽

Structural Damage ◽

Optimization Problems ◽

Linear Matrix ◽

Matrix Inequality ◽

Test Bed ◽

Stiffness Reduction

In this work, linear matrix inequality (LMI) methods are proposed for computationally efficient solution of damage detection problems in structures. The structural damage detection problem that is considered consists of estimating the existence, location, and extent of stiffness reduction in structures using experimental modal data. This problem is formulated as a convex optimization problem involving LMI constraints on the unknown structural stiffness parameters. LMI optimization problems have low computational complexity and can be solved efficiently using recently developed interior-point methods. Both a matrix update and a parameter update formulation of the damage detection is provided in terms of LMIs. The presence of noise in the experimental data is taken explicitly into account in these formulations. The proposed techniques are applied to detect damage in simulation examples and in a cantilevered beam test-bed using experimental data obtained from modal tests. [S0739-3717(00)00104-5]

Study on Damage Location of Spatial Structures Based on Wavelet Analysis of Model Strain Energy

10.4028/www.scientific.net/amr.639-640.1033 ◽

2013 ◽

Vol 639-640 ◽

pp. 1033-1037

Author(s):

Yong Mei Li ◽

Bing Zhou ◽

Guo Fu Sun ◽

Bo Yan Yang

Keyword(s):

Wavelet Analysis ◽

Damage Detection ◽

Structural Damage ◽

Strain Energy ◽

Damage Identification ◽

Damage Index ◽

Modal Strain Energy ◽

Wavelet Method ◽

Long Span

The research to identify and locate the damage to the engineering structure mainly aimed at some simple structure forms before, such as beam and framework. Damage shows changes of local characteristics of the signal, while wavelet analysis can reflect local damage traits of the signal in time domain and frequency domain. For confirming the validity and applicability of structural damage identification methods, wavelet analysis is used to spatial structural damage detection. The wavelet analysis technique provides new ideas and methods of spatial steel structural damage detection. Based on the theory of wavelet singularity detection，with the injury signal of modal strain energy as structural damage index，the mixing of the modal strain energy and wavelet method to identify and locate the damage to the spatial structure is considered. The multiplicity of the bars and nodes can be taken into account, and take the destructive and nondestructive modal strain energy of Kiewitt-type reticulated shell with 40m span as an example of numerical simulation，the original damage signal and the damage signal after wavelet transformation is compared. The location of the declining stiffness identified by the maximum of wavelet coefficients，analyzed as signal by db1 wavelet，and calculate the graph relation between coefficients of the wavelets and the damage to the structure by discrete or continuous wavelet transform, and also check the accuracy degree of this method with every damage case. Finally，the conclusion is drawn that the modal strain energy and wavelet method to identify and locate the damage to the long span reticulated shell is practical, effective and accurate, that the present method as a reliable and practical way can be adopted to detect the single and several locations of damage in structures.

Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

Journal of Civil Engineering and Construction ◽

10.32732/jcec.2020.9.1.14 ◽

2020 ◽

Vol 9 (1) ◽

pp. 14-23 ◽

Cited By ~ 1

Author(s):

Meisam Gordan ◽

Zubaidah Binti Ismail ◽

Hashim Abdul Razak ◽

Khaled Ghaedi ◽

Haider Hamad Ghayeb

Keyword(s):

Data Mining ◽

Exact Solution ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Structural Damage ◽

Damage Identification ◽

Optimization Problems ◽

Engineering Optimization ◽

Structural Health ◽

Structural Damage Identification

In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms.

Flexibility-Based Objective Functions for Constrained Optimization Problems on Structural Damage Detection

10.4028/www.scientific.net/amr.186.383 ◽

2011 ◽

Vol 186 ◽

pp. 383-387 ◽

Cited By ~ 4

Author(s):

Xi Chen ◽

Ling Yu

Keyword(s):

Damage Detection ◽

Constrained Optimization ◽

Objective Function ◽

Structural Damage ◽

Optimization Problems ◽

Objective Functions ◽

Constrained Optimization Problems ◽

Modal Assurance Criterion ◽

Damage Scenarios

Based on concepts of structural modal flexibility and modal assurance criterion (MAC), a new objective function is defined and studied for constrained optimization problems (COP) on structural damage detection (SDD) in this paper. Compared with traditionally objective function, which is defined based on natural frequencies and MAC, effect of objective functions on robustness of SDD calculation is evaluated through numerical simulation of a 2-storey rigid frame. Structural damages are identified by solving the COP on SDD based on an improved particle swarm optimization (IPSO) algorithm. Weak and multiple damage scenarios are mainly considered in various noise conditions. Some illustrated results show that the newly defined objective function is better than the traditional ones. It can be used to identify the damage locations but also to quantify the severity of weak and multiple damages in measurement noise conditions.

A Coupled Sensitivity Method for Structural Damage Detection

10.4028/www.scientific.net/amr.681.271 ◽

2013 ◽

Vol 681 ◽

pp. 271-275

Author(s):

Jing Li ◽

Pei Jun Wei

Keyword(s):

Damage Detection ◽

Mode Shape ◽

Structural Damage ◽

Damage Identification ◽

Perturbation Approach ◽

Beam Structure ◽

Flexibility Matrix ◽

Eigenvalue Sensitivity ◽

Sensitivity Method

Based on the vibration information, a mixed sensitivity method is presented to identify structural damage by combining the eigenvalue sensitivity with the generalized flexibility sensitivity. The sensitivity of structural generalized flexibility matrix is firstly derived by using the first frequency and the corresponding mode shape only and then the eigenvalue sensitivity together with the generalized flexibility sensitivity are combined to calculate the elemental damage parameters. The presented mixed perturbation approach is demonstrated by a numerical example concerning a simple supported beam structure. It has been shown that the proposed procedure is simple to implement and may be useful for structural damage identification.

Structural Damage Detection Using Curvature Mode Difference Curve

10.4028/www.scientific.net/amr.250-253.1248 ◽

2011 ◽

Vol 250-253 ◽

pp. 1248-1251 ◽

Cited By ~ 1

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.

ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2 ◽

Structural Damage Identification Based on Information Fusion Techniques

10.1115/smasis2010-3920 ◽

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 ◽

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.