기계적 학습의 알고리즘을 이용하여 아파트 공사에서 반복 공정의 효과 비교에 관한 연구

• 영문명: Identifying the Effects of Repeated Tasks in an Apartment Construction Project Using Machine Learning Algorithm
• 발행기관: 한국BIM학회
• 저자명: 김현주(Kim Hyun joo)
• 간행물 정보: 『KIBIM Magazine』6권 4호, 35~41쪽, 전체 7쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2016.12.30

영문 초록

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

목차

Abstract

1. Introduction

2. Literature Review

3. Methodology

4. Case Study

5. Conclusions

References

키워드

기계적 학습 컴퓨터 시뮤레이션 학습 효과 의사결정 나무 Machine Learning Computer Simulation Learning Effect Decision Tree