## 6 Conclusions  
The most often used PSC box girder bridge can cause voids in the duct when concrete is poured or when it is maintained for an extended period. In this study, we propose an unsupervised CNN AE method for detecting such voids. For training, 98% of the normal data and 2% of the void data are randomly sampled. To effectively use the CNN model, the IE signal obtained from the manufactured specimen is converted into a wavelet transformed scalogram image. The purpose of AE is to reduce the difference between the input and output, and this difference is estimated utilizing histogram similarity. As a conclusion, because most of the training data are normal data, data with voids differ from normal data. When the histogram similarity distribution of each data set is compared using mean, \(-\sigma\), and \(-2\times\sigma\), the data above the boundary are generally normal data, whereas the data below the boundary are mostly void data. This helps in the detection of voids for actual PSC girder bridges where no correct label is given.  
The difference between normal and void data was validated in this study. However, in the end, a model that can distinguish between normal or void data is required for the given data. The following study should be undertaken in the future to help with this. To begin, we want to design a pattern that can distinguish between normal and void data only when the similarity is less than a specific degree. Due to the nature of field data, a classification model based on unsupervised learning is required. Second, rather than the test specimen, actual bridge data must also be colloected. The location and severity of defects on actual bridges vary greatly. Therefore, data reflecting the diversity of real-world sectors are necessary for more precise learning.  
However, this study is the first approach toward a non-teaching learning method that targets actual data rather than test specimens. It is uncertain whether the prediction will work for actual equipment and objects, even if the prediction is made with high accuracy by experimenting with simulations and test specimens, as in existing papers. As a result, the first approach in this study requires additional experiments, and is critical.  
Figure 12: (**left**) shows a similarity distribution plot of images converted to Gramian angular fields (GAF), and (**right**) shows a similarity distribution plot of images converted to Markov transition fields (MTF). Compare_ssim() was used to evaluate both approaches.  
**Author Contributions:** Conceptualization, Y.-S.K. and H.C.; data curation, H.C.; formal analysis, D.-L.L., J.-D.K. and C.-Y.P.; funding acquisition, Y.-S.K. and H.C.; methodology, D.-L.L.; project administration, Y.-S.K.; resources, C.-Y.P. and J.-D.K.; supervision, Y.-S.K.; validation, D.-L.L.; writing--original draft, D.-I.L.; writing--review and editing, Y.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology development Program (G21S297332301) funded by the Ministry of SMEs and Startups (MSS, Korea), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C2006010), Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) [No. 2021-0-02068, Artificial Intelligence Innovation Hub (Seoul National University)], and the 'R&D Program for Forest Science Technology (Project No. 2021390A00-2123-0105)' funded by Korea Forest Service (KFPI).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.