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.

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.

scholarly journals Identification of Structural Damage in Bridges Using High-Frequency Vibrational Responses

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ivana Mekjavić
Keyword(s):  
High Frequency ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Damage Model ◽  
Reinforced Concrete Beam ◽  
Computational Technique ◽  
Damage Scenarios ◽  
Identification Approach ◽  
Girder Bridge

The present research aims to develop an effective and applicable structural damage detection method. A damage identification approach using only the changes of measured natural frequencies is presented. The structural damage model is assumed to be associated with a reduction of a contribution to the element stiffness matrix equivalent to a scalar reduction of the material modulus. The computational technique used to identify the damage from the measured data is described. The performance of the proposed technique on numerically simulated real concrete girder bridge is evaluated using imposed damage scenarios. To demonstrate the applicability of the proposed method by employing experimental measured natural frequencies this technique is applied for the first time to a simply supported reinforced concrete beam statically loaded incrementally to failure. The results of the damage identification procedure show that the proposed method can accurately locate the damage and predict the extent of the damage using high-frequency (here beyond the 4th order) vibrational responses.

Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models

2020 ◽  
Vol 20 (11) ◽  
pp. 2050124
Author(s):  
Jilin Hou ◽  
Zhenkun Li ◽  
Qingxia Zhang ◽  
Łukasz Jankowski ◽  
Haibin Zhang
Keyword(s):  
Data Fusion ◽  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Identification Accuracy ◽  
Fe Model ◽  
Local Damage ◽  
Approximate Models ◽  
Structural Damage Identification

In practical civil engineering, structural damage identification is difficult to implement due to the shortage of measured modal information and the influence of noise. Furthermore, typical damage identification methods generally rely on a precise Finite Element (FE) model of the monitored structure. Pointwise mass alterations of the structure can effectively improve the quantity and sensitivity of the measured data, while the data fusion methods can adequately utilize various kinds of data and identification results. This paper proposes a damage identification method that requires only approximate FE models and combines the advantages of pointwise mass additions and data fusion. First, an additional mass is placed at different positions throughout the structure to collect the dynamic response and obtain the corresponding modal information. The resulting relation between natural frequencies and the position of the added mass is sensitive to local damage, and it is thus utilized to form a new objective function based on the modal assurance criterion (MAC) and [Formula: see text]-based sparsity promotion. The proposed objective function is mostly insensitive to global structural parameters, but remains sensitive to local damage. Several approximate FE models are then established and separately used to identify the damage of the structure, and then the Dempster–Shafer method of data fusion is applied to fuse the results from all the approximate models. Finally, fractional data fusion is proposed to combine the results according to the parametric probability distribution of the approximate FE models, which allows the natural weight of each approximate model to be determined for the fusion process. Such an approach circumvents the need for a precise FE model, which is usually not easy to obtain in real application, and thus enhances the practical applicability of the proposed method, while maintaining the damage identification accuracy. The proposed approach is verified numerically and experimentally. Numerical simulations of a simply supported beam and a long-span bridge confirm that it can be used for damage identification, including a single damage and multiple damages, with a high accuracy. Finally, an experiment of a cantilever beam is successfully performed.

Damage Identification in Collocated Structural Systems Using Structural Markov Parameters

2013 ◽  
Author(s):  
Ramin Bighamian ◽  
Hamid Reza Mirdamadi ◽  
Jin-Oh Hahn
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Mode Shapes ◽  
Structural Systems ◽  
Structural System ◽  
Computationally Efficient ◽  
Markov Parameters ◽  
Novel Approach ◽  
Stiffness Matrices ◽  
Stiffness Characteristics

This paper presents a novel approach to damage identification in a class of collocated multi-input multi-output structural systems. In the proposed approach, damage is identified via the structural Markov parameters obtained from a system identification procedure, which is in turn exploited to localize and quantify damage by evaluating relative changes occurring in the mass and stiffness matrices associated with the structural system. To this aim, an explicit relationship between structural Markov parameters versus mass and stiffness matrices is developed. The main strengths of the proposed approach are that it is capable of quantitatively identifying the occurrence of multiple damages associated with both mass and stiffness characteristics in the structural system, and it is computationally efficient in that it is solely based on the structural Markov parameters but does not necessitate costly calculations related to natural frequencies and mode shapes, making it highly attractive for structural damage detection and health monitoring applications. Numerical examples are provided to demonstrate the validity and 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.

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.

Damage Identification in Collocated Structural Systems Using Structural Markov Parameters

2014 ◽  
Vol 137 (4) ◽  
Author(s):  
Ramin Bighamian ◽  
Hamid Reza Mirdamadi ◽  
Jin-Oh Hahn
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Mode Shapes ◽  
Structural Systems ◽  
Structural System ◽  
Computationally Efficient ◽  
Markov Parameters ◽  
Novel Approach ◽  
Stiffness Matrices ◽  
Stiffness Characteristics

This paper presents a novel approach to damage identification in a class of collocated multi-input multi-output structural systems. In the proposed approach, damage is identified via the structural Markov parameters obtained from a system identification procedure, which is in turn exploited to localize and quantify damage by evaluating relative changes occurring in the mass and stiffness matrices associated with the structural system. To this aim, an explicit relationship between structural Markov parameters versus mass and stiffness matrices is developed. The main strengths of the proposed approach are that it is capable of quantitatively identifying the occurrence of multiple damages associated with both mass and stiffness characteristics in the structural system, and it is computationally efficient in that it is solely based on the structural Markov parameters but does not necessitate costly calculations related to natural frequencies and mode shapes, making it highly attractive for structural damage detection and health monitoring applications. Numerical examples are provided to demonstrate the validity and effectiveness of the proposed approach.

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