# Ontological Approach for Automatic Inference of Concrete Crack Cause
## 2 Literature Review
### Previous Research on Crack Diagnosis  
Previous studies have been conducted to improve the limits of crack diagnosis discussed above. First, studies have been conducted to extract the visual information of cracks more accurately than the results of human visual surveys by improving the ineffi \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Investigation Item**} & **Impact** & **Error** \\ \hline \multirow{6}{*}{**Crack Status**} & Generation Period & 3.87 & 5.60 \\  & Crack Location & 6.02 & 3.87 \\  & Crack Patterns (Complexity, Shape, Regularity) & 6.27 & 5.20 \\  & Penetration status & 5.69 & 4.89 \\  & Occurrence of defects (Exposure/Corrosion, & 5.58 & 4.01 \\  & Leak/Efflorescence) & 3.40 & 2.32 \\ \hline \multirow{6}{*}{**Materials and construction conditions**} & Condition of concrete materials & 3.86 & 2.02 \\  & Condition of concrete compaction and curing & 3.34 & 3.01 \\ \cline{1-1}  & Records of Concrete Quality Test & 3.26 & 2.10 \\ \cline{1-1}  & ground conditions & 3.02 & 3.34 \\ \cline{1-1}  & Formworks & 3.35 & 3.01 \\ \cline{1-1}  & Environmental conditions during construction & 3.23 & 3.48 \\ \hline \multirow{2}{*}{**Use/Environment Condition**} & Load Condition & 4.43 & 3.59 \\ \cline{1-1}  & Temperature and humidity conditions & 4.35 & 3.04 \\ \cline{1-1}  & Conditions of exposure to chloride & 4.05 & 3.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact and error occurrence of investigation item. ciency of the crack visual investigation method in the crack investigation stage [1; 9; 11; 12; 13; 14; 15]. These researchers argued that manual work by inspectors is laborious, time-consuming, and influenced by subjective behavior of individual inspectors, so objective examination by computer is necessary, and suggested how to efficiently extract visual information such as the detection, patterns, and distribution characteristics of cracks by applying image processing technology or deep learning technology for objective investigation. In most early studies, crack features were extracted and detected using morphological methods, which are image processing techniques utilizing the images' morphological computation [9; 10; 16; 17; 18]. In other studies, researchers have applied a fuzzy technique to the RGB (Red, Green, Blue) channel values in images by utilizing the contrast in the intensity of the cracks [12; 19], improving the contrast characteristics of images by applying a histogram stretching method [20], and using image processing methods such as edge detection and noise removal [21; 22; 23; 24]. More recently, with the emergence of artificial intelligence and deep learning technology, studies applying CNN (convoluted neural network) techniques to crack detection have also been conducted [25; 26; 27; 28; 29; 30; 31]. The focus of most of the above studies was on collecting objective and error-free visual information by automation based on images to improve the inefficiencies of existing visual inspection methods by the naked eye through the automatic collection of visual information on cracks. Recently, there have been attempts to apply video technology for safety inspections of buildings in the practice of facility maintenance. Nonetheless, few studies have been conducted on reducing the subjectivity of experts in the stage of estimating the crack cause. In these studies, the researchers pointed out that since the crack diagnosis practice is based on the subjective knowledge of experts, the diagnosis results can be different from expert to expert, and because the crack generation mechanism is very complex, non-experts with insufficient knowledge and experience cannot quickly and easily estimate the cause of the crack. To address these limitations, [PERSON] et al.