An Optimal Hybrid Expansion—Reduction Damage Detection Method

2003 ◽  
Vol 9 (8) ◽  
pp. 983-995 ◽  
Author(s):  
M. Abdalla ◽  
K. Grigoriadis ◽  
D. Zimmerman
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Linear Matrix ◽  
Test Bed ◽  
Detection Problem ◽  
Lmi Optimization ◽  
Modal Data ◽  
Stiffness Matrices

In this paper, we examine the structural damage detection problem with an incomplete set of measurements. Linear matrix inequality (LMI) optimization methods are proposed to solve this hybrid damage detection problem that integrates modal data expansion and model reduction with an LMI based damage detection procedure. In the proposed hybrid approach, the transformation matrix is based on the measured data avoiding the use of the healthy mass and stiffness matrices. The method is demonstrated using experimental modal data obtained from the NASA eight-bay cantilevered truss test bed. The experimental results of this hybrid approach are shown to provide a clearer indication of damage than using stand-alone expansion or reduction techniques.

Structural Damage Detection Using Linear Matrix Inequality Methods

2000 ◽  
Vol 122 (4) ◽  
pp. 448-455 ◽  
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 ◽  
Structural Damage Detection ◽  
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]

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.

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.

Damage Identification in Continuum Structures From Vibration Modal Data

1997 ◽  
Vol 503 ◽  
Author(s):  
H. P. Chen ◽  
N. Bicanic
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Computational Technique ◽  
Gauss Point ◽  
Continuum Structures ◽  
Modal Data ◽  
Stiffness Matrices ◽  
Direct Iteration ◽  
Vibration Modal

ABSTRACTA novel procedure for damage identification of continuum structures is proposed, where both the location and the extent of structural damage in continuum structures can be correctly determined using only a limited amount of measurements of incomplete modal data. On the basis of the exact relationship between the changes of structural parameters and modal parameters, a computational technique based on direct iteration and directly using incomplete modal data is developed to determine damage in structure. Structural damage is assumed to be associated ith a proportional (scalar) reduction of the original element stiffness matrices, equivalent to a scalar reduction of the material modulus, which characterises at Gauss point level. Finally, numerical examples for plane stress problem and plate bending problem are utilised to demonstrate the effectiveness of the proposed approach.

LS-SVM Regression for Structural Damage Diagnosis Using the Iterated Improved Reduction System

2016 ◽  
Vol 16 (06) ◽  
pp. 1550018 ◽  
Author(s):  
S. S. Kourehli
Keyword(s):  
Structural Damage ◽  
Estimation Method ◽  
Mode Shapes ◽  
Support Vector ◽  
Tuning Parameters ◽  
Modal Data ◽  
Reduction System ◽  
Stiffness Matrices ◽  
Svm Model ◽  
Coupled Simulated Annealing

A damage detection and estimation method is proposed for structural health monitoring using incomplete modal data and least squares support vector machine (LS-SVM). To accommodate the use of incomplete modal data, the iterated improved reduction system (IIRS) method has been used to condense the mass and stiffness matrices of the structure. The first two incomplete mode shapes and natural frequencies of a damaged structure are used as input data to the LS-SVM. The coupled simulated annealing (CSA) and standard simplex method using 10-fold cross-validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM model. Three illustrative examples with and without noise in modal data are prepared to evaluate the performance of the proposed method. The results indicated that this method can be reliably used to identify the damages of structures with good accuracy.

scholarly journals Boundary Strategy for Optimization-based Structural Damage Detection Problem using Metaheuristic Algorithms

2020 ◽  
Author(s):  
Ali Kaveh ◽  
Seyed Milad Hosseini ◽  
Ataollah Zaerreza
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Optimization Method ◽  
Metaheuristic Algorithms ◽  
Structural Elements ◽  
Detection Problem ◽  
Vibration Data ◽  
New Strategy ◽  
Optimum Solution ◽  
Better Than

The present paper proposes a new strategy namely Boundary Strategy (BS) in the process of optimization-based damage detection using metaheuristic algorithms. This strategy gradually neutralizes the effects of structural elements that are healthy in the optimization process. BS causes the optimization method to find the optimum solution better than conventional methods that do not use the proposed BS. This technique improves both aspects of the accuracy and convergence speed of the algorithms in identifying and quantifying the damage. To evaluate the performance of the developed strategy, a new damage-sensitive cost function, which is defined based on vibration data of the structure, is optimized utilizing the Shuffled Shepherd Optimization Algorithm (SSOA). Different examples including truss, beam, and frame are investigated numerically in order to indicate the applicability of the proposed technique. The proposed approach is also applied to other well-known optimization algorithms including TLBO, GWO, and MFO. The obtained results illustrate that the proposed method improves the performance of the utilized algorithms in identifying and quantifying of the damaged elements, even for noise-contaminated data.

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