scholarly journals Optimal Placement of Virtual Masses for Structural Damage Identification

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 340
Author(s):  
Jilin Hou ◽  
Zhenkun Li ◽  
Qingxia Zhang ◽  
Runfang Zhou ◽  
Łukasz Jankowski
Keyword(s):  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Pso Algorithm ◽  
Optimal Placement ◽  
Beam Structure ◽  
Sensitivity Matrix ◽  
Swarm Optimization ◽  
Placement Optimization ◽  
Mutual Independence

Adding virtual masses to a structure is an efficient way to generate a large number of natural frequencies for damage identification. The influence of a virtual mass can be expressed by Virtual Distortion Method (VDM) using the response measured by a sensor at the involved point. The proper placement of the virtual masses can improve the accuracy of damage identification, therefore the problem of their optimal placement is studied in this paper. Firstly, the damage sensitivity matrix of the structure with added virtual masses is built. The Volumetric Maximum Criterion of the sensitivity matrix is established to ensure the mutual independence of measurement points for the optimization of mass placement. Secondly, a method of sensitivity analysis and error analysis is proposed to determine the values of the virtual masses, and then an improved version of the Particle Swarm Optimization (PSO) algorithm is proposed for placement optimization of the virtual masses. Finally, the optimized placement is used to identify the damage of structures. The effectiveness of the proposed method is verified by a numerical simulation of a simply supported beam structure and a truss structure.

Finite Element Model Updating Approach to Damage Identification in Beams Using Particle Swarm Optimization

2008 ◽  
Author(s):  
Mohamed M. Saada ◽  
Mustafa H. Arafa ◽  
Ashraf O. Nassef
Keyword(s):  
Finite Element ◽  
Particle Swarm Optimization ◽  
Finite Element Model ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Model Updating ◽  
Particle Swarm ◽  
Element Model ◽  
Pso Algorithm ◽  
Swarm Optimization

The use of vibration-based techniques in damage identification has recently received considerable attention in many engineering disciplines. While various damage indicators have been proposed in the literature, those relying only on changes in the natural frequencies are quite appealing since these quantities can conveniently be acquired. Nevertheless, the use of natural frequencies in damage identification is faced with many obstacles, including insensitivity and non-uniqueness issues. The aim of this paper is to develop a viable damage identification scheme based only on changes in the natural frequencies and to attempt to overcome the challenges typically encountered. The proposed methodology relies on building a Finite Element Model (FEM) of the structure under investigation. A modified Particle Swarm Optimization (PSO) algorithm is proposed to facilitate updating the FEM in accordance with experimentally-determined natural frequencies in order to predict the damage location and extent. The method is tested on beam structures and was shown to be an effective tool for damage identification.

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.

scholarly journals Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab

2020 ◽  
Vol 10 (8) ◽  
pp. 2869 ◽  
Author(s):  
Zhenpeng Wang ◽  
Minshui Huang ◽  
Jianfeng Gu
Keyword(s):  
Elastic Modulus ◽  
Reinforced Concrete ◽  
Ambient Temperature ◽  
Temperature Effects ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Mode Shapes ◽  
Arx Model ◽  
Rc Slab

To study the variations in modal properties of a reinforced concrete (RC) slab (such as natural frequencies, mode shapes and damping ratios) under the influence of ambient temperature, a laboratory RC slab is monitored for over a year, the simple linear regression (LR) and autoregressive with exogenous input (ARX) models between temperature and frequencies are established and validated, and a damage identification based on particle swarm optimization (PSO) is utilized to detect the assumed damage considering temperature effects. Firstly, the vibration testing is performed for one year and the variations of natural frequencies, mode shapes and damping ratios under different ambient temperatures are analyzed. The obtained results show that the change of ambient temperature causes a major change of natural frequencies, which, on the contrary, has little effect on damping ratios and modal shapes. Secondly, based on a theoretical derivation analysis of natural frequency, the models are determined from experimental data on the healthy structure, and the functional relationship between temperature and elastic modulus is obtained. Based on the monitoring data, the LR model and ARX model between structural elastic modulus and ambient temperature are acquired, which can be used as the baseline of future damage identification. Finally, the established ARX model is validated based on a PSO algorithm and new data from the assumed 5% uniform damage and 10% uniform damage are compared with the models. If the eigenfrequency exceeds the certain confidence interval of the ARX model, there is probably another cause that drives the eigenfrequency variations, such as structural damage. Based on the constructed ARX model, the assumed damage is identified accurately.

Damage Identification for Beam Structure Based on Correlation Dimension

2012 ◽  
Vol 193-194 ◽  
pp. 1342-1345
Author(s):  
Mao Jiang ◽  
Ling Zhou ◽  
Ying Tao Li ◽  
Hai Qing Zhou ◽  
Jun Shao
Keyword(s):  
Structural Damage ◽  
Correlation Dimension ◽  
Damage Identification ◽  
Fractal Theory ◽  
Vibration Signal ◽  
Structural Vibration ◽  
Damage Analysis ◽  
Beam Structure ◽  
Damage Degree ◽  
System Output

In order to explore the effective damage identification method for structure, the structural vibration signal is directly correlation dimension analyzed according to fractal theory, and structural damage is identified by measuring the singularity in system output, then the method for structural damage identification based on correlation dimension of vibration response is proposed. The damage analysis results of a simply supported beam demonstrate that, the proposed method can accurately detect single and multi different degree damage’s location of beam structure, and alteration of correlation dimension will increase along with the damage degree

A Coupled Sensitivity Method for Structural Damage Detection

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 ◽  
Structural Damage Detection ◽  
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.

A Comparative Study on Inverse Vibration Based Damage Assessment Techniques in Beam Structure Using Ant Colony Optimization and Particle Swarm Optimization

2020 ◽  
Vol 12 (7) ◽  
pp. 918-923
Author(s):  
Aditi Majumdar ◽  
Bharadwaj Nanda
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Damage Assessment ◽  
Damage Identification ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Soft Computing Techniques ◽  
Frequency Changes

Use of swarm intelligence has proliferated over previous couple of years for damage assessment in large and complex structures using vibration data. Available literatures shows ‘ant colony optimization’ (ACO) and ‘particle swarm optimization’ (PSO) are predominantly used for solving complex engineering problems including damage identification and quantification problems. The time requirement and accuracy of the vibration based damage identification algorithms depends on early exploration and late exploitation capabilities of soft computing techniques. However, there are not any literature available comparing algorithms on these bases. In the current study, an inverse problem is constructed using the natural frequency changes which is then solved using ACO and PSO algorithms. The algorithm is run for identification of single and multiple damages in simple support and cantilever beam structures. It's found that, both ACO and PSO based algorithms are capable of detecting and quantifying the damage accurately within the limited number of iterations. However, ACO based algorithm by virtue of its good exploration capability is able to identify near optimal region faster than PSO based algorithm, whereas PSO algorithm has good exploitation capability and hence able to provide better damage quantification than ACO algorithm at latter stages of iteration. Further, PSO based algorithm takes less time to reach at required accuracy level. It is also observed that, the time required for these algorithms are independent of numbers of damage and support conditions.

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