Detection of Defect Inside Duct Using Recurrent Neural Networks

Oh, Byoung-Doo; Choi, Hyung Woo; Song, Hey-Jeong; Kim, Jong-Dae; Park, Chan-Young; Kim, Yu-Seop

doi:10.18494/sam.2020.2578

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…In [10], a simulation using electrical impulses to determine normality and defects is used. In addition, in [11], a LSTM model is used to detect cavities in impact-echo time series data acquired by non-destructive testing.…”

Section: Ae Methods For Anomaly Detectionmentioning

confidence: 99%

Utilization of Unsupervised Machine Learning for Detection of Duct Voids inside PSC Box Girder Bridges

et al. 2022

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The PSC box girder bridge is a pre-stressed box girder bridge that accounts for a considerable part of large-scale bridges. However, when concrete is poured, even small mistakes might result in voids that appear during long-term maintenance. In this paper, we present a technique for detecting the void in the duct inside the PSC box girder bridge. Data are acquired utilizing the non-destructive impact-echo (IE) approach to detect these voids. IE creates time-series data as signal data initially; however, we want to use a CNN auto-encoder (AE). A scalogram, which is a kind of wavelet transformation, is used to convert time series data into an image. An AE is a type of unsupervised learning that aims to minimize the difference between the input and output. By comparing histograms, the difference is calculated. To begin, we create scalogram images from all IE signal data, which were randomly sampled as 98% normal and 2% void. The CNN AE is then trained and evaluated utilizing all the data. Finally, we examine the input and output histogram similarity distributions. As a consequence, only 4% of the normal data had a similarity of less than two standard deviations from the mean, whereas 34.7% of the void data did. As a result, the existence of voids inside the PSC duct could be demonstrated to be predictive in the absence of annotated data.

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Section: Ae Methods For Anomaly Detectionmentioning

confidence: 99%

Utilization of Unsupervised Machine Learning for Detection of Duct Voids inside PSC Box Girder Bridges

et al. 2022

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“…However, although internal void detection of ducts is a very important problem, many studies have mainly used signalprocessing-based approaches and machine learning has been rarely applied. (28) Oh et al (28) trained a standardized raw IE signal and structure information (concrete thickness, depth of duct, distance between the measured point and impact point) with LSTM to detect the internal voids of ducts. The raw IE signal contains various noises caused by the environment.…”

Section: Related Workmentioning

confidence: 99%

Nondestructive Evaluation of Ducts in Prestressed Concrete Bridges Using Heterogeneous Neural Networks and Impact-echo

Oh¹,

Choi²,

Kim³

et al. 2022

Sensors and Materials

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Prestressed concrete (PSC) girder bridges are widely used owing to their economic efficiency, durability, and effective maintenance. However, since voids in ducts may cause sudden structural collapse, it is very important to detect them early. To solve this problem, voids are detected by analyzing the impact-echo (IE) signal measured by IE equipment containing a sensor, but it is difficult to accurately detect voids in a short time even by experts. In this study, we aim to detect voids in ducts on the basis of various types of neural networks and IE signals. For more effective learning, the raw IE signal is filtered and then used in its specific range, and it is also converted into a frequency spectrum by the Fourier transform. The filtered IE signal is trained with long short-term memory (LSTM) to reflect the characteristics of its time series. The frequency spectrum is trained with a feed-forward neural network because it is not a time series. After that, a multiplication operation is performed on the outputs of each network, and a model capable of detecting the internal voids of ducts is created by training these integrated features. In the experimental results, our proposed model showed an accuracy of 97.474%.Yu-Seop Kim received his Ph.D. degree in computer engineering from Seoul National University, Republic of Korea. He is currently a professor in the School of Software, Hallym University. His research interests are in the areas of bioinformatics, computational intelligence, and natural language processing.

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“…Detecting voids that arise inside the ducts of PSC bridges is a particularly significant issue; however, it has only been investigated using a signal processing-based method, and machine learning is rarely applied. Therefore, by utilizing IE data and LSTM, supervised learning was attempted to detect defects inside ducts in previous studies [26][27][28]. Utilizing IE data and structural information (concrete thickness, depth of duct, distance between the measured point and impact point), [26] used LSTM to classify the duct's voids.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, by utilizing IE data and LSTM, supervised learning was attempted to detect defects inside ducts in previous studies [26][27][28]. Utilizing IE data and structural information (concrete thickness, depth of duct, distance between the measured point and impact point), [26] used LSTM to classify the duct's voids. However, we determined that the structural information affects the scalability of the deep learning-based approach.…”

Section: Introductionmentioning

confidence: 99%

Void Detection inside Duct of Prestressed Concrete Bridges Based on Deep Support Vector Data Description

Choi²,

Chin

et al. 2023

Applied Sciences

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The tendon that is inserted into the duct is a crucial component of prestressed concrete (PSC) bridges and, when exposed to air, can quickly corrode, and cause structural collapse. It can interpret the signal measured by non-destructive testing (NDT) to determine the condition (normal or void) inside the duct. However, it requires the use of expensive NDT equipment such as ultrasonic waves or the hiring of experts. In this paper, we proposed an impact–echo (IE) method based on deep support vector data description (Deep SVDD) for economical void detection inside a duct. Because the pattern of IE changes for various reasons such as difference of specimen or bridge, supervised learning is not suitable. Deep SVDD is classified as normal and defective, which is a broad distribution as a hypersphere that encloses a multi-dimensional feature space for normal data represented by an autoencoder. Here, an autoencoder was developed based on the ELMo (embeddings from language model)-like structure to obtain an effective representation for IE. In the experiment, we evaluated the performance of the IE data measured in different specimens. Thus, our proposed model showed an accuracy of about 77.84% which is an improvement of up to about 47% compared to the supervised learning approach.

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Detection of Defect Inside Duct Using Recurrent Neural Networks

Cited by 9 publications

References 11 publications

Utilization of Unsupervised Machine Learning for Detection of Duct Voids inside PSC Box Girder Bridges

Utilization of Unsupervised Machine Learning for Detection of Duct Voids inside PSC Box Girder Bridges

Nondestructive Evaluation of Ducts in Prestressed Concrete Bridges Using Heterogeneous Neural Networks and Impact-echo

Void Detection inside Duct of Prestressed Concrete Bridges Based on Deep Support Vector Data Description

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