## 6 On-Site Application and Verification of the Automated PC Member Crack Detection System
### Verification of the System through Field Application  
For the verification through field application of the automated PC member crack detection system proposed in this study, a construction site involving PC members was visited, where video images of cracks and non-cracks on the surface of PC members (e.g., PC slab and column) were recorded and collected. The construction site involved PC construction of an underground parking structure for a reconstruction housing project. Specific project information is summarized in Table 3 below. Figure 14: Field application scenario for smart quality inspection system. Using a mobile device camera, 2000 crack and non-crack images each were collected from the PC members (i.e., slabs and columns) at the verification site. Table 4 below shows sample images of crack and non-crack, respectively, from the total of 4000 image data. After data collection, the original crack image data were labeled, and a training dataset was prepared so as to proceed with CNN-based image training. Table 5 summarizes the composition of the original crack data, the crack data for training, and the original non-crack data used in this study. To proceed with the training, a CNN-based neural network was used, and the training was carried out based on a server with CPU Intel i9-9900k, GPU GeForce RTX 2080 Ti, and 64 GB memory. As summarized in Table 6, crack training showed 78% recall and 75% precision, whereas test showed 75% recall and 71% precision, both cases indicating somewhat lower precision than recall. However, recall and precision for both cases resulted in a high percentage of 70% or more. In addition, by setting the crack width 0.4 mm and the length 3.0 mm as threshold values, only cracks above the threshold values were classified and detected. Moreover, quantitative information on the width and length of cracks was also obtained. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Object** & **Sample Image** & **Number of Images** \\ \hline Crack Image & & 2000 \\ (PC slab and column) & & \\ Non-crack Image & & 2000 \\ (FC slab and column) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample images of crack and non-crack taken using a mobile device camera. \begin{table}
\begin{tabular}{c c} \hline \hline  & **PC Construction Site Overview** \\ \hline Project Name & Gwangmyeong Reorganization District Housing Reconstruction Project \\ Project Duration & & 20 February 2020–28 October 2022 \\ Project Location & S. Korea, Gyeonggi-do, Gwangmyeong-si, Saeteo-ro 45 \\ Construction Method & PC construction \\ Construction Task & Underground parking structure PC construction (Fabrication and installation) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the PC construction site for the system field application. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training** & **Test** \\ \hline Crack data & 1600 & 400 \\ \hline Non-crack data & 1600 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Composition of the image data for training and test. Comparing the crack width and length measured visually with those measured using the automated crack detection system presented in this study revealed that the crack width had an error in the range of 0.01 mm to 0.02 mm in Cracks 1 and 2. As for the crack length, an error of 0.1 mm occurred in Cracks 3 and 4. Specific size measurement values for each crack case are presented in Table 7. The images of the crack detection result are shown in Figure 15.  
Comparing the time taken for each step of the detection process using the existing on-site PC member quality inspection process versus the automated crack detection system, the work time during the inspection preparation phase was reduced by six minutes as it took one minute of preparation time for the automated system compared to the seven minutes of work time for the existing on-site inspection process.