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Sensors and Materials, Volume 32, Number 1(2) (2020)
Copyright(C) MYU K.K.
pp. 171-182
S&M2094 Research Paper of Special Issue
Published: January 20, 2020

Detection of Defect Inside Duct Using Recurrent Neural Networks [PDF]

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

(Received July 30, 2019; Accepted December 6, 2019)

Keywords: defect detection, long short-term memory, impact echo, PSC box-girder bridge, deep learning

Prestressed concrete (PSC) box-girder bridges are grouted after inserting a tendon in the duct in order to protect the tendon from the risk of corrosion. However, because of the small inside diameter of the duct, it is difficult to completely fill it with concrete (grout) and even small mistakes can cause defects. Today, the complex and professional analysis of the signals measured by nondestructive testing (NDT) is conducted by a geophysicist to detect defects. However, owing to the limitations of NDT, accurate detection is very difficult. We introduce a deep learning model for defect detection to help working-level officials in the field immediately perform defect detection without the need for such complex and professional analysis. In this study, we apply the long short-term memory (LSTM), which is one of the deep learning models with good performance for time series data. Moreover, we use raw impact echo (IE) signals measured using IE equipment and some characteristics of the bridge (concrete thickness, depth of duct, and distance between the measured point and hit point). At this time, because the value of the signal was very small, we standardized the raw data to perform normalization. As a result of the experiment, we obtained an average accuracy of 82.58%.

Corresponding author: Yu-Seop Kim

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Byoung-Doo Oh, Hyung Choi, Hey-Jeong Song, Jong-Dae Kim, Chan-Young Park, and Yu-Seop Kim, Detection of Defect Inside Duct Using Recurrent Neural Networks, Sens. Mater., Vol. 32, No. 1, 2020, p. 171-182.

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