## 5 Conclusions  
In this paper, we used IE and Deep SVDD which is semi-supervised learning for detecting voids inside the ducts of PSC bridges and tried to address the following issues. We verified that the patterns of IE data measured in different specimens or PSC bridges differed. Here, we developed an autoencoder with an ELMo-like LSTM architecture to represent the feature space of the IE data well to effectively use Deep SVDD. To demonstrate the reliability of our proposed method, we compared the performance with supervised learning models. In the first experiment, we evaluated how well the supervised learning model performed using data measured in the same specimen, and it showed good performance. Next, we compared the performance of our proposed model with that of the supervised learning model on trained data and tested data in different specimens. The experiment revealed that our proposed model performed better on average than the supervised learning model by around 25.6% (up to about 47%), with an accuracy of about 77.84%. Thus, we believe that the Deep SVDD-like semi-supervised learning can be effectively applied to real-world anomaly detection tasks. However, it is considered that the performance of our proposed model is insufficient to be used for actual industry purposes, and a process of confirming it with IE data measured from more diverse specimens and actual PSC bridges is considered necessary.  
In the future, we will systematically study the unsupervised anomaly detection approach and autoencoder architecture to develop a void detection technique inside the duct of PSC bridges that is more effective and superior to what is now available. Furthermore, we will find a way to collect IE data from real PSC bridges.  
Conceptualization, Y.-S.K. and H.C.; methodology, B.-D.O.; validation, C.-Y.P., H.C. and W.-J.C.; formal analysis, C.-Y.P, W.-J.C. and B.-D.O.; data curation, B.-D.O.; supervision, Y.-S.K.; project administration, Y.-S.K.; funding acquisition, Y.-S.K.; writing--original draft preparation, B.-D.O.; writing--review and editing, Y.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Hallym University Research Fund, 2022 (HRF-202204-003).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**True**} & \multicolumn{2}{c}{**Pred**} & \multirow{2}{*}{**Acc (\%)**} \\ \cline{3-3} \cline{5-6}  & & **Normal** & & **Void** & \\ \hline \multirow{2}{*}{[PERSON] et al. [26]} & Normal & 665 & 2791 & 19.24 & \multirow{2}{*}{56.96} \\  & Void & 2043 & 5732 & 73.72 & \\ \hline \multirow{2}{*}{[PERSON] et al. [27]} & Normal & 6 & 3450 & 0.17 & \multirow{2}{*}{68.99} \\  & Void & 32 & 7743 & 99.59 & \\ \hline \multirow{2}{*}{[PERSON] et al. [28]} & Normal & 3456 & 0 & 100.0 & \multirow{2}{*}{30.77} \\  & Void & 7775 & 0 & 0.0 & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Proposed** \\ **model** \\ \end{tabular} } & Normal & **3456** & 0 & **100.0** & \multirow{2}{*}{**77.84**} \\  & Void & 2489 & **5286** & **67.99** & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of data collected in different environments by supervised learning models and our proposed model.