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아파트 개발사업의 추진 단계별 수익 리스크 영향요인 분석
손승현(Son, Seunghyun),오창현(Oh, ChangHyeon),오창연(Oh, ChangYeon),이동찬(Lee, DongChan),장건(Jang, Geon),김지명(Kim, Ji-Myong) 대한건축학회 2024 대한건축학회 학술발표대회 논문집 Vol.44 No.1
The purpose of this study is to analyze the factors influencing the profit risk of each stage of the apartment development project. There are a wide variety of factors that influence the success or failure of the apartment development project. Key factors affecting profits are land cost, construction cost, unit sales price, sales ratio, and financial cost. These change over time. Revenue, which is a business performance, fluctuates due to changes in factors. In this study, the characteristics of five key factors were identified, and the concept of profit risk management was established for efficient management of risk factors that change over time. By developing the results of this study, we plan to develop a profit risk management model using software.
AI기반 건설현장의 외국인 근로자 안전사고 예측을 위한 기본 연구
김지명 ( Kim Ji-myong ),이준혁 ( Lee Junhyeok ),김경빈 ( Kim Gyeongbin ),오창현 ( Oh Changhyeon ),오창연 ( Oh Changyeon ),손승현 ( Son Seunghyun ) 한국건축시공학회 2023 한국건축시공학회 학술발표대회 논문집 Vol.23 No.2
Compared to other industries the construction industry experiences more casualties and property damage due to safety accidents. One of the reasons is the increasing number of foreign workers. For this reason, past studies have found that foreign workers at construction sites are more exposed to safety accidents than non-foreign workers. Nevertheless the proportion of foreign workers involved in safety accidents at construction sites is increasing, and there has been a lack of research to predict the risk of safety accidents at construction sites. Additionally, realistic safety management is lacking due to a lack of safety accident risk prediction research. Therefore, in this study, we would like to propose basic research that proposes an AI-based safety accident prediction model framework for predicting safety accidents of foreign workers at construction sites. The framework and results of this study will contribute to reducing and preventing the risk of safety accidents for foreign workers through risk prediction for safety management of foreign workers at construction sites.