# Ontological Approach for Automatic Inference of Concrete Crack Cause
## 2 Literature Review
### Previous Research on Crack Diagnosis
[3] proposed a system to estimate the causes of cracks occurring in reinforced concrete structures by taking into account nine variables related to cracks (shape, occurrence time, depth, weather, reinforcement corrosion, depth of neutralization, alkali-aggregate reactivity, chloride content in concrete, and humidity). In addition, they used these variables to model a system through data access objects (DAO) to estimate the cause of the cracks and secured objectivity for crack diagnosis work through this system. However, it is difficult to apply these crack diagnosis techniques in practice due to the unrealistic input variables. Among the variables they considered, occurrence time, weather, and chloride content in concrete were information items collected through the documentation survey, and as discussed in Section 2.2, related documents such as construction records for structures have often been misplaced or do not exist, making it difficult to obtain this information. In addition, the form and distribution of cracks were classified simply as single/complex and radial/network/negative in this study. However, in reality, the shape and distribution of cracks vary much more, and it is very important to know which member the crack is located in when estimating the cause of the crack, which the researchers did not take into account. Meanwhile, [PERSON] et al. [4] proposed a computer-aided system to diagnose the causes of cracks in reinforced concrete structures. They comprehensively considered seven variables (generation period, shape, regularity, cause of concrete deformation, range, weather conditions during concrete placement, and conditions of concrete mixing) presented by the JCI (Japanese Concrete Institute) in relation to crack symptoms by applying the fuzzy set theory to develop a system to help non-experts to diagnose the causes of cracks. This was more applicable in practice because of more detailed classifications of information related to the shape and distribution of cracks that were more simply classified in the study [PERSON] et al. [3]. However, some of the input variables considered in their study are as unrealistic as those in [PERSON] et al. [3]. For example, information such as generation period, weather conditions during concrete placement, and the conditions of concrete mixing are difficult to reflect unless past construction records exist. In addition, the cause of production (contraction, expansion, setting bending shear) and range (material, member, structure) should be estimated and entered through subjective judgments by experts, so there is a high chance that the output results will vary from user to user. In the above studies, the researchers proposed measures to reduce the subjective intervention of experts who estimated the causes of cracks by automating the estimation process while considering a number of variables comprehensively in relation to crack occurrence. However, some of the input variables considered in the above studies are difficult to collect in practice, which is a drawback as users estimate them through subjective judgment for input. Although research that solves problems arising from the crack investigation process has been achieved in practice, research to solve problems arising from estimating the causes of cracks based on the results of the crack investigation is insufficient. Therefore, in this study, we want to focus on solving problems in which different experts make different diagnoses or non-experts fail to make accurate diagnoses because the work to estimate the cause of the cracks depends on the expert knowledge and experience. Thus, the aim in the present study is to propose an automatic inference of crack causes in concrete based on ontology using four pieces of information (crack location, pattern, and penetration or not, and other defects around the crack) in relation to crack appearance, which significantly affects the estimation of crack causes and have a high level of error, to overcome the limitations of previous studies.