# Ontological Approach for Automatic Inference of Concrete Crack Cause
## 5 Conclusions  
Concrete cracks have traditionally been estimated by analyzing information such as crack patterns and location, and whether other defects are present, and then aggregating the findings to estimate the cause of the cracks. This method is highly dependent on the experts' knowledge and experience in the process of identifying the cause of cracks by compiling the information related to their occurrence, and it was likely that each expert will make a different diagnosis or an expert with insufficient knowledge and experience will make an inaccurate diagnosis. Therefore, in this research, we propose automated technology using ontology to improve the consistency and accuracy of crack diagnosis results. Although the information on the crack condition (pattern, location, etc.) has the greatest effect on the accuracy of the crack cause estimation, in many cases it relies only on visual inspection, so many estimation errors occur. To solve this problem, this study presented a methodology to automate the process of estimating the cause of the crack based on the crack state. To automate inference of crack cause, we proposed \"Crack Type Ontology (CTO),\" which consists of factors that determine the type of crack, and \"Crack Cause Ontology (CCO),\" which consists of factors that define the cause of the crack. To verify the consistency and accuracy of the built the CTO and the CCO, eight actual cases of cracks were collected, and the results derived from the proposed ontology were compared with the diagnosis results derived by the five crack diagnosis practitioners. Because an ontology is a technology that allows machines to express human thinking mechanisms and supports logical inference, the proposed approach can remove the matter of complex qualitative guidance that occurs during the crack cause estimation process. As such, the approach in this study can support expert decision-making in the crack diagnosis process, thereby reducing the possibility of various errors due to the qualitative judgment in the crack diagnosis process and improving the efficiency of crack diagnosis work. As a result, it can contribute to the automation of the crack diagnosis process and the improvement of the consistency and reliability of the diagnosis results. Future studies to improve the completion of this study are as follows. First, the accuracy of the reasoning results needs to be improved. We selected the factors necessary for estimating the cause of cracks and established the inference rules through consideration of the existing literature and expert interviews. Inference rules may vary somewhat from expert to expert, so further expert interviews are needed in future studies to increase the accuracy to the extent available in practice. Second, the future research is needed to develop a system that considers the strength class of concrete, the cement content and the age of the concrete etc. In this study, we proposed ontology to estimate the cause of the crack by using information related to the shape of the crack, but information such as the strength class of concrete, the cement content, the age of the concrete, etc. also greatly affect the formation