# Ontological Approach for Automatic Inference of Concrete Crack Cause
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{4}{c}{**Traditional Method (Expert Decision)**} & \multicolumn{2}{c}{**Proposed Method**} \\ \cline{2-7}  & **A (9 Years)** & **B (10 Years)** & **C (11 Years)** & **D (17 Years)** & **E (20 Years)** & **(Using Ontology)** \\ \hline \multirow{4}{*}{**Case 5**} & A9 (80\%) & A9 (80\%) & B11 (20\%) & A2 (20\%) & A9 (80\%) & A9 (100\%) \\  & B5 (20\%) & D1 (40\%) & D1 (40\%) & A9 (80\%) & B12 (40\%) & B10 (100\%) \\  & B7 (20\%) & D2 (20\%) & D5 (40\%) & B12 (40\%) & D5 (40\%) & B12 (100\%) \\  & D2 (60\%) & D2 (60\%) & D5 (40\%) & B4 (20\%) & D2 (60\%) & B4 (100\%) \\
**Case 6** & D3 (20\%) & D4 (60\%) & B12 (20\%) & B5 (20\%) & D4 (60\%) & B5 (100\%) \\  & E (40\%) & D6 (20\%) & E (40\%) & D4 (60\%) & D5 (40\%) & D4 (100\%) \\  & A9 (60\%) & D2 (60\%) & A9 (60\%) & A9 (60\%) & A9 (60\%) & D2 (60\%) & A9 (100\%) \\
**Case 7** & B8 (20\%) & D4 (60\%) & D1 (20\%) & D2 (60\%) & D4 (60\%) & D2 (100\%) \\  & B10 (20\%) & D5 (20\%) & D2 (20\%) & D4 (60\%) & E (20\%) & D4 (100\%) \\  & D2 (60\%) & D1 (20\%) & A9 (20\%) & D2 (60\%) & D2 (60\%) & D2 (100\%) \\
**Case 8** & D4 (80\%) & D3 (20\%) & D5 (60\%) & D4 (80\%) & D4 (80\%) & D4 (100\%) \\  & D6 (40\%) & D4 (80\%) & D6 (40\%) & D5 (60\%) & D5 (60\%) & D5 (100\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(Cont.\)of cracks. Third, to fully automate the inference process, additional research is needed to automatically extract and input information entered into the ontology. Information entered into the ontology presented in this study should be entered manually. Further research is needed to eliminate the possibility of errors that may occur when entering information manually and to automate the crack diagnosis process as a whole. This could be solved by using drones to acquire images, using deep learning and image processing technologies to automatically detect cracks, and by linking them with technologies that can automatically extract information such as the shape and penetration of cracks, as well as the presence of other defects. Finally, further research is needed to establish an integrated data exchange environment that can be managed with various information of buildings by linking crack investigation data and estimated crack cause data with building information modeling (BIM). Cracks are deeply related to the spatial and structural elements of the building, but most crack data are text-based in PDF, Word, and spreadsheet documents, which can be dispersed [41]. This makes it difficult for multiple stakeholders, including building owners, facility managers, builders, and structural engineers, to search or analyze data. These limitations can be resolved by integrating the crack data with spatial information in the BIM, thereby making more accurate diagnosis and the sharing and reuse of crack cause data obtained through crack cause estimation ontology possible. Conceptualization, S.J., S.L. and J.Y. ; methodology, S.J. and S.L. ; software. S.L. ; validation. S.J. and S.L. ; formal analysis, S.J. and S.L. ; investigation, S.J. ; resources, J.Y. ; data curation, J.Y. ; writing-original draft preparation, S.J. ; writing-review and editing, S.L. and J.Y. ; visualization, S.L. ; supervision, J.Y. ; project administration, J.Y. ; funding acquisition, J.Y. ; All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020 RIF1A107517711). And the present research has been conducted by the Research Grant of Kwangwoon University in 2020. Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.