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http://dx.doi.org/10.13161/kibim.2016.6.4.035

Identifying the Effects of Repeated Tasks in an Apartment Construction Project Using Machine Learning Algorithm  

Kim, Hyunjoo (서울시립대학교 글로벌건설학과)
Publication Information
Journal of KIBIM / v.6, no.4, 2016 , pp. 35-41 More about this Journal
Abstract
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.
Keywords
Machine Learning; Computer Simulation; Learning Effect; Decision Tree;
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