| Title |
Development of SVM-based Construction Project Document Classification Model to Derive Construction Risk |
| Authors |
강동욱(Kang, Donguk) ; 조민건(Cho, Mingeon) ; 차기춘(Cha, Gichun) ; 박승희(Park, Seunghee) |
| DOI |
https://doi.org/10.12652/Ksce.2023.43.6.0841 |
| Keywords |
공기 지연; 건설 재해; 데이터 분류; 텍스트 마이닝; SVM Construction delays; Construction accident; Data classification; Text mining; Machine learning |
| Abstract |
Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management. |