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.

VIBRATION-BASED STRUCTURAL DAMAGE DETECTION UNDER VARYING TEMPERATURE CONDITIONS

2013 ◽  
Vol 13 (05) ◽  
pp. 1250082 ◽  
Author(s):  
XIAO-QING ZHOU ◽  
WEN HUANG
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Environmental Changes ◽  
Model Updating ◽  
Modal Testing ◽  
Structural Damage Detection ◽  
Vibration Data ◽  
Temperature Conditions ◽  
Vibration Properties

In vibration-based structural damage detection, it is necessary to discriminate the variation of structural properties due to environmental changes from those caused by structural damages. The present paper aims to investigate the temperature effect on vibration-based structural damage detection in which the vibration data are measured under varying temperature conditions. A simply-supported slab was tested in laboratory to extract the vibration properties with modal testing. The slab was then damaged and the modal testing was conducted again, in which the temperature varied. The modal data measured under different temperature conditions were used to detect the damage with a two-stage model updating technique. Some damage was falsely detected if the temperature variation was not considered. Natural frequencies were then corrected to those under the same temperature conditions according to the relation between the temperature and material modulus. It is shown that all of the damaged elements can be accurately identified.

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.

scholarly journals Damaged Flexibility Matrix Method for Damage Detection of Frame Buildings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Luis S. Vaca Oyola ◽  
Mónica R. Jaime Fonseca ◽  
Ramsés Rodríguez Rocha
Keyword(s):  
Damage Detection ◽  
Matrix Method ◽  
Structural Damage ◽  
Numerical Models ◽  
Structural Elements ◽  
Structural Element ◽  
New Approach ◽  
Building Model ◽  
Flexibility Matrix ◽  
Frame Buildings

This study presents the damaged flexibility matrix method (DFM) to identify and determine the magnitude of damage in structural elements of plane frame buildings. Damage is expressed as the increment in flexibility along the damaged structural element. This method uses a new approach to assemble the flexibility matrix of the structure through an iterative process, and it adjusts the eigenvalues of the damaged flexibility matrices of each system element. The DFM was calibrated using numerical models of plane frames of buildings studied by other authors. The advantage of the DFM, with respect to other flexibility-based methods, is that DFM minimizes the adverse effect of modal truncation. The DFM demonstrated excellent accuracy with complete modal information, even when it was applied to a more realistic scenario, considering frequencies and modal shapes measured from the recorded accelerations of buildings stories. The DFM also presents a new approach to simulate the effects of noise by perturbing matrices of flexibilities. This approach can be useful for research on realistic damage detection. The combined effects of incomplete modal information and noise were studied in a ten-story four-bay building model taken from the literature. The ability of the DFM to assess structural damage was corroborated. Application of the proposed method to a ten-story four-bay building model demonstrates its efficiency to identify the flexibility increment in damaged structural elements.

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

2020 ◽  
pp. 147592172094283 ◽  
Author(s):  
Zhiqiang Shang ◽  
Limin Sun ◽  
Ye Xia ◽  
Wei Zhang
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Deep Neural Networks ◽  
Beam Model ◽  
Structural Damage Detection ◽  
Denoising Autoencoder ◽  
Vibration Data

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

A Shannon Entropy-Based Methodology to Detect and Locate Cables Loss in a Cable-Stayed Bridge

2021 ◽  
Vol 13 (05) ◽  
Author(s):  
Jose M. Machorro-Lopez ◽  
Martin Valtierra-Rodriguez ◽  
Juan P. Amezquita-Sanchez ◽  
Francisco J. Carrion-Viramontes ◽  
Juan A. Quintana-Rodriguez
Keyword(s):  
Fuzzy Logic ◽  
Damage Detection ◽  
Shannon Entropy ◽  
Structural Damage ◽  
Recognition Algorithm ◽  
Economic Losses ◽  
Entropy Index ◽  
Vibration Data ◽  
Cable Stayed Bridge ◽  
Cable Stayed Bridges

As with any civil structure or mechanism, vehicular bridges can suffer structural damages which can conduct to devastating human and economic losses if they are not detected and corrected on time. In this work, a methodology based on the Shannon entropy index combined with statistical indexes and a fuzzy logic classifier to detect and locate a cable loss in cable-stayed bridges is proposed. Shannon entropy index is used to characterize the changes in the vibration signals associated with structural damage, which are integrated with statistical indexes for damage detection and damage location. On the other hand, the fuzzy logic classifier is used as a pattern recognition algorithm to detect structural damage automatically. For this study, the vibration data acquired experimentally from the Rio Papaloapan Bridge (Veracruz, Mexico) are analyzed. Results demonstrate the usefulness of the proposed method since 93.3% of effectiveness in the damage detection is obtained with a 100% of effectiveness in its location.

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.

scholarly journals A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

2013 ◽  
Vol 20 (4) ◽  
pp. 633-648 ◽  
Author(s):  
Zahra Tabrizian ◽  
Ehsan Afshari ◽  
Gholamreza Ghodrati Amiri ◽  
Morteza Hossein Ali Beigy ◽  
Seyed Mohammad Pourhoseini Nejad
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Element Model ◽  
Optimization Method ◽  
Big Bang ◽  
Continuous Variable ◽  
Particle Swarm Optimizer ◽  
Damage Scenarios ◽  
Damage Location ◽  
Big Crunch

The present paper aims to explore damage assessment methodology based on the changes in dynamic parameters properties of vibration of a structural system. The finite-element model is used to apply at an element level. Reduction of the element stiffness is considered for structural damage. A procedure for locating and quantifying damaged areas of the structure based on the innovative Big Bang-Big Crunch (BB-BC) optimization method is developed for continuous variable optimization. For verifying the method a number of damage scenarios for simulated structures have been considered. For the purpose of damage location and severity assessment the approach is applied in three examples by using complete and incomplete modal data. The effect of noise on the accuracy of the results is investigated in some cases. A great unbraced frame with a lot of damaged element is considered to prove the ability of proposed method. More over BB-BC optimization method in damage detection is compared with particle swarm optimizer with passive congregation (PSOPC) algorithm. This work shows that BB-BC optimization method is a feasible methodology to detect damage location and severity while introducing numerous advantages compared to referred method.

scholarly journals Combined Data with Particle Swarm Optimization for Structural Damage Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Kang ◽  
Junjie Li ◽  
Sheng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Damage Detection ◽  
Structural Damage ◽  
Detection Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Simulated Annealing Genetic Algorithm ◽  
Combined Data ◽  
Better Than

This paper proposes a damage detection method based on combined data of static and modal tests using particle swarm optimization (PSO). To improve the performance of PSO, some immune properties such as selection, receptor editing, and vaccination are introduced into the basic PSO and an improved PSO algorithm is formed. Simulations on three benchmark functions show that the new algorithm performs better than PSO. The efficiency of the proposed damage detection method is tested on a clamped beam, and the results demonstrate that it is more efficient than PSO, differential evolution, and an adaptive real-parameter simulated annealing genetic algorithm.

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