## 7 Conclusions and Future Research  
In this study, a mobile and web server-based automated PC member crack detection system that can automatically detect cracks on the exterior of a PC member was proposed. For system construction and field application, the on-site quality inspection items, process, and standards for PC members were analyzed, and a deep learning-based PC member smart quality inspection algorithm was designed. Additionally, system verification was performed by establishing a smart quality inspection process and scenario for the on-site application of the automated PC member smart quality inspection system. Through this system, it is possible to more efficiently and accurately inspect the quality of the PC members brought to the construction site, compared to the existing on-site inspection that commonly relies on one's visual check and experience. In addition, the reliability of the quality inspection checklist items is secured through the use of deep learning-based crack detection, whereas the work efficiency and convenience are improved through the inspection information management by means of a web server-based inspection information database. The main results derived from this study are as follows:  
1. Derivation of key items for on-site quality inspection of PC members and classification of inspection items applicable to AI;
2. Analysis of problems involving the as-is process of on-site quality inspection of PC members and classification of work steps in which IT technology is applicable;
3. Design of the smart quality inspection algorithm for PC members and system construction;
4. Establishment of the smart quality inspection process and field application scenario for the system field application;
5. Reduction of work time compared to as-is quality inspection process through field application of smart quality inspection system;
6. Improvement of work accuracy and efficiency through smart quality inspection system-based quality inspection data storage and management;
7. Support of OSC precise construction and establishment of systematic OSC project management structure through the application of the smart quality inspection system. Since OSC projects were the target of the smart system presented in this study, verification, optimization, and improvement of the system are warranted through application on diverse construction sites. Moreover, enhancement of the defect detection accuracy can be achieved by varying deep learning model training and testing based on factors that affect defect detection, such as deep learning model and parameter, image data quantity, and quality. In future research, based on the automated PC member crack detection system proposed in this study, not only cracks in PC members but also automatic recognition and classification of member defects, such as wear, breakage, and discoloration, can be developed. Moreover, research on automatic reporting of user's quality inspection checklists based on automated defect detection of the PC members can be conducted. It is considered that research on automatic reporting technology is possible. Furthermore, in connection with various smart construction technologies, such as BIM and platform technology, a study on the automation of quality and history management for the production-transport-construction-maintenance stage of the OSC site is necessary, while the research \begin{table}
\begin{tabular}{c c c} \hline \hline
**Task Title** & **As-Is Quality Inspection Process (min)** & **Smart Quality Inspection Process (min)** \\ \hline Inspection Preparation & 7 & 1 \\ Inspection & 7 & 2 \\ Review Inspection Result & 3 & 1 \\ Total & 17 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of inspection time between as-is process versus smart quality inspection process. on automation of quality control for various construction materials, not limited to the PC member, is also considered necessary. Conceptualization, S.L. and M.J.; methodology, S.L. and S.K. ; validation, S.L. and C.-S.C.; formal analysis, S.L.