[논문]건설 리스크 도출을 위한 SVM 기반의 건설프로젝트 문서 분류 모델 개발

강동욱; 조민건; 차기춘; 박승희

doi:10.12652/ksce.2023.43.6.0841

건설 리스크 도출을 위한 SVM 기반의 건설프로젝트 문서 분류 모델 개발
Development of SVM-based Construction Project Document Classification Model to Derive Construction Risk 원문보기

KSCE Journal of Civil and Environmental Engineering Research = 대한토목학회논문집, v.43 no.6, 2023년, pp.841 - 849

강동욱 (성균관대학교 글로벌스마트시티융합공학과) , 조민건 (성균관대학교 글로벌스마트시티융합공학과) , 차기춘 (성균관대학교 글로벌스마트시티융합공학과) , 박승희 (성균관대학교 건설환경공학부)

초록
AI-Helper

건설프로젝트는 공기 지연, 건설 재해 등 다양한 요인으로 인한 리스크가 존재한다. 이러한 건설 리스크를 기반으로 건설프로젝트의 공사 기간의 산정 방법은 주로 감독자 경험에 의존한 주관적 판단으로 이루어지고 있다. 또한, 공기 지연과 건설 재해로 지연된 건설프로젝트 일정을 맞추기 위한 무리한 단축 시공은 부실시공 등의 부정적인 결과를 초래하며, 지연된 일정으로 인한 사회 기반 시설물 부재로 경제적 손실이 발생한다. 이러한 건설프로젝트의 리스크 해결을 위한 데이터 기반의 과학적 접근과 통계적 분석이 필요한 실정이다. 실제 건설프로젝트에서 수집되는 데이터는 비정형 텍스트 형태로 저장되어 있어 데이터를 기반으로 한 리스크를 적용하기 위해서는 데이터 전처리에 많은 인력과 비용을 수반하기 때문에 텍스트 마이닝을 활용한 데이터 분류 모델을 통한 기초자료를 요구한다. 따라서, 본 연구에서는 건설프로젝트 문서를 수집하여 텍스트 마이닝을 활용하여 SVM(Support Vector Machine) 기반의 데이터 분류 모델을 통해 리스크 관리를 위한 문서 기초자료 생성 분류 모델을 개발하였다. 향후 연구 결과를 통해 정량적인 분석을 통해서 건설프로젝트 공정관리 등에 있어 효율적이고 객관적인 기초자료로 활용되어 리스크 관리가 가능해질 것으로 기대된다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (6)

그림 Fig. 1. Research Flow Chart to Document Classification Model
그림 Fig. 2. Definition of the Confusion Matrix
표 Table 1. Construction Work Breakdown Structure (WBS) Classification Criteria
표 Table 2. Predictive Performance of Machine Learning Algorithms Using Data Normalization
그림 Fig. 3. Average Performance Metrics for Models
그림 Fig. 4. Confusion Matrix for Proposed Model

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