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신한우 ( Han-woo Shin ),서장우 ( Jang-woo Soe ),조훈희 ( Hun-hee Cho ),강경인 ( Kyung-in Kang ) 한국건축시공학회 2006 한국건축시공학회 학술발표대회 논문집 Vol.6 No.1
Since the diversification, complication of the construction industry, construction conflicts have been increased these days. Expansion of scale of construction has brought conflict treatment cost increase. Thus, needs of the study on construction conflict risk factors and its management is getting important. But the existing studies about construction conflicts and risk factors that need to be cared to decrease construction cost are almost focused on analysis of number of conflict proposed. It means those studies can helpful when want to know how many conflict have been instituted, however, they do not contain a close examination between types of conflict and cost of conflict treatment. The purpose of this study, therefore, is an extraction of conflict causes based on the conflict treatment cost and the choice of significant risk factors to be managed to prevent conflict and its treatment cost, which enables reducing the conflict cost on control the risk factors.
신경망을 이용한 흙막이 지보공공법 선정모델 개발에 관한 연구
김재엽(Kim Jae-Youp),서장우(Soe Jang-Woo),강경인(Kang Kyung-In) 대한건축학회 2003 大韓建築學會論文集 : 構造系 Vol.19 No.5
As a construction project in urban area tends to be high-rise and huge, the importance of the project's underground work, in terms of the cost and the schedule, is increasing gradually. The selection of a suitable shoring method is most important in this underground work. However, in Korea, because the design and the construction parts of the shoring works are separated, many changes of design have occurred and the changes have effects on the cost and the schedule of the project. In this study, we have suggested a decision model for shoring method that can be used to determine the suitable method in planning and design phase of a project. Based on history data, a neural network model was proven to be efficient. The tests of the model for decision of suitable shoring method by using data which were not used in the learning process of neural network showed that accuracy of the selections is up to 77%.