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

Author:

Oh Byoung-Doo1ORCID,Choi Hyung2,Chin Won-Jong3ORCID,Park Chan-Young4ORCID,Kim Yu-Seop4ORCID

Affiliation:

1. Cerebrovascular Disease Research Center, Hallym University, Chuncheon-si 24252, Gangwon-do, Republic of Korea

2. AI Bridge Co., Ltd., Gwangjin-gu, Seoul 05117, Republic of Korea

3. Department of Structural Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Gyeonggi-do, Republic of Korea

4. Division of Software, College of Information Science, Hallym University, Chuncheon-si 24252, Gangwon-do, Republic of Korea

Abstract

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.

Funder

Hallym University Research Fund

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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