Isomap-based damage classification of cantilevered beam using modal frequency changes

Jeong, Minjoong; Choi, Jong-Hun; Koh, Bong-Hwan

doi:10.1002/stc.1587

Cited by 12 publications

(7 citation statements)

References 25 publications

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“…The purpose of damage-type identification is to determine the specific type of damage present in the structure. 17,18 The damage-type identification problem, when executed through a data-driven approach, becomes a multiclass classification problem. In machine learning, there are several categories of methods for performing multiclass classification.…”

Section: Error Correcting Output Codes (Ecoc) For Damage Type Identif...mentioning

confidence: 99%

Semi‐supervised implementation of SVM‐based error‐correcting output code for damage‐type identification in structures

Agrawal

Chakraborty

2022

Structural Contr & Hlth

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Supervised learning techniques are the most commonly used type of learning techniques for damage-type identification tasks. A necessary condition for these supervised learning approaches to be successful is the availability of a sufficient amount of labeled data corresponding to various possible damaged and undamaged states of the structure. Unfortunately, in several applications, it is either impossible or very costly to acquire sufficient labeled data for all damage scenarios; however, unlabeled data are available in plenty. It will be of great use if one can utilize these unlabeled data to improve the performance of supervised damage identification methods. Semi-supervised implementation of support vector machine (SVM) based error-correcting output code proposed in this paper iteratively converts unlabeled data into labeled data in a principled way to increase the number of labeled data for training, thereby improving the performance of the damage-type identification method. The performance of the proposed technique has been validated on several experimental ASCE benchmark datasets obtained from a 4-story 2-bay by 2-bay steel frame. It was observed that the damage-type identification using semi-supervised implementation outperforms its supervised counterpart in terms of generalization as measured using the five-fold cross-validation error. The aforementioned comparison of the five-fold cross-validation errors was performed after the hyperparameters of both supervised and unsupervised methods were chosen optimally. The conditions for the applicability of the semi-supervised method have also been discussed briefly.

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Section: Error Correcting Output Codes (Ecoc) For Damage Type Identif...mentioning

confidence: 99%

Semi‐supervised implementation of SVM‐based error‐correcting output code for damage‐type identification in structures

Agrawal

Chakraborty

2022

Structural Contr & Hlth

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“…Non-linear dimensionality reduction techniques became very popular in the last decade due to their superior performance on high dimensional data when compared to linear techniques [ 28 ]. Jeong [ 29 ] showed that ISOMAP outperforms PCA (a linear dimensionality reduction technique) on high dimensional data.…”

Section: Proposed Systemmentioning

confidence: 99%

Nondestructive Inspection of Reinforced Concrete Utility Poles with ISOMAP and Random Forest

Ullah

Jeong

Lee³

2018

Sensors

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Reinforced concrete poles are very popular in transmission lines due to their economic efficiency. However, these poles have structural safety issues in their service terms that are caused by cracks, corrosion, deterioration, and short-circuiting of internal reinforcing steel wires. Therefore, they must be periodically inspected to evaluate their structural safety. There are many methods of performing external inspection after installation at an actual site. However, on-site nondestructive safety inspection of steel reinforcement wires inside poles is very difficult. In this study, we developed an application that classifies the magnetic field signals of multiple channels, as measured from the actual poles. Initially, the signal data were gathered by inserting sensors into the poles, and these data were then used to learn the patterns of safe and damaged features. These features were then processed with the isometric feature mapping (ISOMAP) dimensionality reduction algorithm. Subsequently, the resulting reduced data were processed with a random forest classification algorithm. The proposed method could elucidate whether the internal wires of the poles were broken or not according to actual sensor data. This method can be applied for evaluating the structural integrity of concrete poles in combination with portable devices for signal measurement (under development).

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“…Dimensionality reduction is very important, because it alleviates undesired properties of high-dimensional spaces, such as "the curse of dimensionality" [5]. In the literature, various dimensionality reduction methods have been proposed: (i) linear methods, such as principal component analysis (PCA) [6,7] and linear discriminant analysis (LDA) [8,9], and (ii) nonlinear methods, such as isometric mapping (ISOMAP) [10,11] and the non-parametric version of t-distributed stochastic neighbor embedding (t-SNE) [12].…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Structural Health Monitoring Using Piezoelectric Transducers and Parametric t-SNE

Agis¹,

Pozo²

2020

Sensors

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In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.

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Isomap-based damage classification of cantilevered beam using modal frequency changes

Cited by 12 publications

References 25 publications

Semi‐supervised implementation of SVM‐based error‐correcting output code for damage‐type identification in structures

Semi‐supervised implementation of SVM‐based error‐correcting output code for damage‐type identification in structures

Nondestructive Inspection of Reinforced Concrete Utility Poles with ISOMAP and Random Forest

Vibration-Based Structural Health Monitoring Using Piezoelectric Transducers and Parametric t-SNE

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