Modal Data Based Approach for Structural Damage Identification by Means of Imperialist Competitive Optimization Algorithm

Dabbagh, Hossein A.; Amiri, Gholamreza Ghodrati; Shaabani, Sh.

doi:10.24200/sci.2017.4590

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…However, in order avoid presenting similar and repetitive outputs, the cases with the crack sizes of 20, 30, and 50 mm are used. Unlike the reference [48], the first two measurements of the acceleration responses in the undamaged (measurements 1-2) and damaged (measurements [11][12] cases are chosen to make the data matrices X and Y. Accordingly, both matrices consist of 8002 samples (rows) and 15 variables (columns).…”

Section: A Numerical Benchmark Concrete Beammentioning

confidence: 99%

“…The central idea of SHM via this strategy lies in the fact that the numerical (updated) and real models of the structure are considered as undamaged and current states. Hence, it is attempted to use information of both models to define a damage equation as an inverse problem and solve it via various mathematical and optimization techniques [8][9][10][11]. By contrast, the data-based methods only utilize measured vibration signals without any FE modeling or updating procedures, and any data transformation from raw time domain into frequency or modal domains.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Damage Location under Statistical Pattern Recognition by New Feature Extraction and Feature Analysis Methods

Feizi,

Sharbatdar,

Mahjoub

et al. 2021

Scientia Iranica

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The main objective of this article is to identify the location of damage by a new feature extraction technique and propose some efficient feature analysis tools as statistical distance measures. The proposed algorithm of feature extraction relies on a combination of the well-known Principal Component Analysis (PCA) and a convolution strategy. After extracting the features from raw vibration signals of undamaged and damaged conditions, those are applied to the proposed feature analysis approaches called coefficient of variation, Fisher criterion, Fano factor, and relative reliability index, all of which are formulated by using statistical moments of the features extracted from the PCA-convolution algorithm. To localize damage, the sensor location with the distance value exceeded from a threshold limit is identified as the damaged area. The main innovations of this research are to present a new hybrid technique of feature extraction suitable for SHM applications and four effective statistical measures for feature analysis and damage identification. The performance and reliability of the proposed methods are verified by a four-story shear-building model and a benchmark concrete beam. Results demonstrate that the approaches presented here can influentially identify the location of damage by using the features extracted from the proposed PCA-convolution algorithm.

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Section: A Numerical Benchmark Concrete Beammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Damage Location under Statistical Pattern Recognition by New Feature Extraction and Feature Analysis Methods

Feizi,

Sharbatdar,

Mahjoub

et al. 2021

Scientia Iranica

View full text Add to dashboard Cite

show abstract

“…To achieve the projected aim, it is required to de ne a suitable objective function that comprises a number of parameters that are sensible enough to identify the whole plausible structural damages [59]. As previously mentioned, the proposed method is of multi-criteria type, and several criteria are used to choose the generation.…”

Section: Fitness Functionmentioning

confidence: 99%

A Genetic Algorithm based framework for mining quantitative association rules without specifying minimum support and minimum confidence

Moslehi

Haeri

2019

Scientia Iranica

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Discovering association rules is a useful and common technique for data mining, in which relations and co-dependencies of datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and con dence criteria. In this paper, a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the genetic algorithm, and there is no need to determine the extent of the threshold for the support and con dence criteria. By proposing a multi-criteria method, useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discretize numerical values and determine the minimum support threshold and minimum con dence threshold. Di erent criteria are considered to determine appropriate rules. In this algorithm, selected rules are extracted based on con dence, interestingness, and cosine 2 . The results obtained from real-world datasets demonstrate the e ectiveness of the proposed approach. The algorithm is used to examine three datasets, and the results show the superior performance of the proposed algorithm compared to similar algorithms.

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A Two-Step Damage Diagnostic Approach Based on Static Strain Energy and Model-Updating via the Equilibrium Optimizer

Safa

Rasouli

Nasira

2022

Shock and Vibration

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Despite the great achievements accomplished so far in the field of damage detection, the need for a more comprehensive damage detection approach that effectively functions despite several real-world obstacles still exists. Among different methods suggested so far, model-updating holds the most appeal; however, its accuracy and efficiency are seriously challenged when the problem size grows as it fails to solve the problem with an increase in the number of variables. In this paper, a two-step approach is suggested that reduces the problem size in model updating-based damage detection methods by identifying potentially damaged elements using a static strain-based damage identification index over the first step. Therefore, only a few unknown variables will be introduced to the next step, which involves an iterative model-updating process that includes a novel damage-sensitive objective function to determine damage severities in the located elements of the previous step. In this stage, a meta-heuristic optimizer, the Equilibrium Optimizer, is utilized to detect the unknown variables, i.e., damage values. This optimization algorithm is used given that it has not been applied to damage detection problems before. The method is also tested on a number of numerical examples to be validated and the effects of external factors such as measurement noise can be taken into account. Apart from that, a comparative study is also carried out to compare the efficacy of the present work with previous references. Interestingly, the method is capable of detecting damaged elements with great speed and accuracy, and the quality of the results has not been seriously affected in the presence of perturbing factors. Moreover, information from even a limited number of first modes has been enough to solve the problem, which means that the approach does not require all vibration modes. Finally, an experimental verification study is also carried out to demonstrate the capability of the method in detecting damaged elements in real structures. It is shown that the method can detect the damaged element successfully, which is indicative of the practicality of the suggested technique in real-world applications.

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Modal Data Based Approach for Structural Damage Identification by Means of Imperialist Competitive Optimization Algorithm

Cited by 3 publications

References 31 publications

Identifying Damage Location under Statistical Pattern Recognition by New Feature Extraction and Feature Analysis Methods

Identifying Damage Location under Statistical Pattern Recognition by New Feature Extraction and Feature Analysis Methods

A Genetic Algorithm based framework for mining quantitative association rules without specifying minimum support and minimum confidence

A Two-Step Damage Diagnostic Approach Based on Static Strain Energy and Model-Updating via the Equilibrium Optimizer

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