S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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

Byoung-Doo Oh, Hyung Choi, Young Jin Kim, Won Jong Chin, and Yu-Seop Kim

(Received May 11, 2021; Accepted November 18, 2021)

Keywords: PSC girder bridge, nondestructive evaluation, defect detection, neural network, impact-echo

Prestressed concrete girder bridges are widely used due 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. This study aims to detect voids in ducts based on 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 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%.

Corresponding author: Yu-Seop Kim

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