과제정보
본 연구는 국토교통부/국토교통과학기술진흥원 과제(과제번호: 1615012983)에 의해 수행되었습니다.
The occurrence of defect, one of the major risk elements, gives rise to construction delays and additional costs. Although construction companies generally prefer to use a method of identifying and classifying the causes of defects, a system for predicting the rise of defects becomes important matter to reduce this harmful issue. However, the currently used methods are kinds of reactive systems that are focused on the defects which occurred already, and there are few studies on the occurrence of defects with prediction systems. This paper is about preliminary study on the development of judgemental algorithm that informs us whether additional works related to defect issue are needed or not. Among machine learning techniques, deep neural network was utilized as prediction model which is a major component of algorithm. It is the most suitable model to be applied to the algorithm when there are 8 hidden layers and the average number of nodes in each hidden layer is 70. Ultimately, the algorithm can identify and defects that may arise in later and contribute to minimize defect frequency.
본 연구는 국토교통부/국토교통과학기술진흥원 과제(과제번호: 1615012983)에 의해 수행되었습니다.