## 5 Results and Utilization Plan
### Utilization Plan  
The proposed model has the potential to aid in identifying potential issues during concrete pouring by detecting formwork impact sounds, which may help mitigate the risk of accidents. Currently, the deep-learning-based classification model focuses on detecting formwork impact sounds during concrete pouring. However, as future work unfolds, it is planned to expand the model to detect more specific accident-related sounds, such as the sound of formwork breakage, by collecting and classifying additional sound data. This expansion is particularly significant given that the deep-learning-based sound classification model emphasizes detecting formwork impact sounds during concrete pouring work. In light of these developments, a sound classification model for concrete pouring work can be utilized at construction sites to analyze sound data and understand the current status of the concrete pouring process, including the timing and productivity of the work. By analyzing sound data from vibrator work, the model can identify the compacting work time and prevent material separation. Furthermore, the model can immediately instruct the manager to stop and supplement the work by identifying the sound of impact on the formwork in real-time. Additionally, the model can be set to issue a warning if it detects loud impact sounds, such as the collapse of formwork or external impacts, by identifying sound data that exceed an acceptable threshold for such noises (Figure 14). Building upon these capabilities, integrating the proposed sound classification model with existing safety management systems at construction sites can lead to a more comprehensive and effective safety monitoring approach. Combining sound data analysis with other sensor data and visual monitoring techniques could provide a more accurate and real-time assessment of potential hazards, ultimately improving overall safety on construction sites. This improvement will enable construction managers and workers to leverage the proposed model to enhance safety practices and procedures by monitoring the sound data in real time and taking appropriate actions to mitigate potential risks. Moreover, the proposed model can contribute to reducing costs associated with accidents, downtime, and delays in construction projects. Early detection of potential issues and timely interventions can help prevent accidents that might result in costly damage to equipment, materials, and structures, as well as injuries to workers. Additionally, by minimizing downtime and delays caused by accidents or safety incidents, the model can contribute to more efficient construction processes and ultimately lead to cost savings for construction companies. Figure 13: CNN classification model result. As a future goal, identifying the cause and type of sound through a deep-learning algorithm based on the measured sound source data after measuring and analyzing various types of sounds generated at the construction site is essential. This will help in developing an IoT device and operating system that predicts and notifies the location of the corresponding area when an abnormal sign is detected (Figure 15). A classification model that can detect abnormal sounds by classifying normal noise at the site was applied in this study because it is difficult to collect and learn sounds of accidents or abnormal signs. The classification model can be continuously upgraded and abnormal sign data can be secured using the accumulated data on the server if all the sound data of the site are transmitted to the server after building a web server based on the classification model. Consequently, the more the system is used on the site, the more robust the system will be. Based on the findings presented in this study, several directions for future research are suggested. First, future researchers should aim to collect and annotate a larger and more diverse dataset of construction site sounds to further improve the accuracy and generalizability of the model.