# Hammering Test for Tile Wall Using Deep Learning
## 5 Conclusions  
This study presents the result of our research findings on using deep learning for hammering tests on tile walls that used adhesive to fix the tiles. This scenario is known to be particularly challenging even for experienced inspectors to identify problems. In prior research, we had already verified the effectiveness of applying deep learning to hammering tests for tile walls using mortar to fix the tiles. In this research, we conducted additional validation to confirm that deep learning techniques can effectively assess structural health by analyzing the friction sounds produced when an inspection stick with a spherical tip rubs against tile walls. This method, frequently employed in inspection settings, provides valuable auditory data for evaluating structural integrity, which our study has demonstrated can be interpreted accurately by deep learning models.  
Moreover, the deep learning technique successfully classified the sound caused by friction using an inspection rod recorded in four classes, such as 100%, 80%, 60%, and 0% adhesion rates. In this study, we have successfully identified unique characteristics that enable the neural network to accurately distinguish between different adhesion rates. These distinctive features serve as key indicators, allowing the model to discern variations in adhesion levels across different samples effectively. These findings underscore the potential for creating a hammering test system capable of handling diverse adhesion rates. By training the model on data that span a broad array of adhesion conditions, such a system could be developed to adapt effectively across varying levels of adhesion.  
Looking ahead, we plan to study adhesion rates in more detail by testing additional samples, such as those with adhesion rates of 20% and 40%. When we analyzed the confusion matrix, we noticed that the misclassified data often clustered around a specific adhesion rate. Interestingly, these mistakes did not always match the closest adhesion rate. This suggests that we need to introduce more precise and effective models to process the sound and also use more powerful machine learning techniques. In the future, we will take a closer look at these errors to better understand what is causing them and improve our methods.  
In the real situation, we need to consider environmental noise such as road noise since we usually check the outsides of buildings. The sound caused by a tile piece is around 5 kHz. However, the road noise is 20 to 500 Hz [26]. A test in the real environment is for a future study. However, we think it is easy to filter road noise.  
Conceptualization, A.I.; methodology, A.I., M.K. and M.S.; software, A.I.; validation, M.K. and M.S.; formal analysis, A.I., M.K. and M.S. resources, K.H.; data curation, M.S.; writing--original draft preparation, A.I.; writing--review and editing, M.K.; supervision, K.H.; project administration, K.H.; funding acquisition, A.I., M.K. and K.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by JSPS Kakenhi (24K14987, 23K28338, 23H03649, 22K12598, 20H05702) and Tokyo Metropolitan Small and Medium Enterprise Support Center.  
This study did not involve humans or animals.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank [PERSON] of Fujiyogyo Co., Ltd., for developing test specimens.  
Conflicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Author [PERSON] is working at FORT DENSHI Corporation, however there is no financial relationships that could be construed as a potential conflict of interest. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.