## 1 Introduction  
Concrete has a high compressive strength but a low flexural strength. Pre-stressed concrete (PSC) is a type of concrete that was created to compensate for these shortcomings. PSC box girder bridges account for more than 90% of all high-speed railway bridges built to date, making them the most often used bridge type. As a result, if the PSC box girder bridge is appropriately constructed, the national SOC project cost may be reduced [1]. However, because the inner diameter of the duct is so tiny, when concrete is poured immediately on site, the construction precision may be reduced. A void inside the duct may also appear if the girder bridge is maintained for an extended period of time. Internal exploration work to verify the stability of the PSC box girder bridge is crucial in order to avoid this.  
The impact-echo (IE) method, which is a non-destructive test, is used to detect voids in concrete. This method uses vibration induced by a short-term mechanical shock to determine if an object is defective. However, even if the signal is interpreted based on professional academic knowledge, it is difficult to find the signal rules; therefore, exact classification is limited. To address this issue, numerous signal classification methods have been developed. In [2], the statistical moments of the signal phase were used to automatically classify the kind of signal. The use of sparse signal representation for signal classification is also mentioned [3]. Deep learning methods have recently attracted much attention. Research was carried out using CNN, a deep learning approach for multi-signalclassification [4]. They presented LSTM deep learning techniques in [5] to increase the electroglottograph (EGG) signal classification accuracy.  
We propose a deep learning approach for detecting the normality and voids inside the PSC box girder bridge's duct. We utilized an auto-encoder (AE), a type of unsupervised learning, because the signal pattern varies according on the environment in which the IE is measured. The method of existing papers is to detect data from the specimen, where the correct answer is determined. For practical application, unless checking directly into the object, the correct answer is never obtained. Therefore, unsupervised learning is applied for this purpose. Under the assumption that there is no correct answer, the goal of this paper is to determine how the data distribution between normal and void data is composed. This study differs from previous studies in that it takes a novel method.  
In this study, we apply a CNN to an auto-encoder in order to transform IE signal data into images and to detect voids, which is unsupervised learning. The method of converting into an image utilizes a scalogram, which is a type of wavelet transformation. In addition, the difference between the input and output images of AE is estimated as the difference between each histogram. Finally, voids in the duct inside the PSC are detected by using the histogram similarity distribution of each image data. AE is trained using 98% randomly sampled normal data and 2% randomly collected void data. The results are assessed with the crucial assumption that the histogram similarity of normal data is greater than that of void data. As a consequence, it was determined that only 4% of the normal data differed by more than two standard deviations from the similarity mean, but 34.7% of the void data differed by more than two standard deviations. We offer a technique for detecting faults using solely unlabeled IE data in ths study.  
The second section introduces previous research instances relevant to this subject. Section 3 includes a discussion of the box girder bridge, as well as an explanation of IE. The learning and prediction model proposed in this study is described in Section 4, and the experimental results of using this model are described in Section 5. Finally, the conclusion is delivered in Section 6, with a future plan.