BIM performance assessment system using a K-means clustering algorithm
Hyeon-Seung Kim, Sung-Keun Kim, Leen-Seok Kang
Abstract
Currently, various guidelines regarding building information modelling (BIM) technology policy are being developed in different countries. However, for many companies, the cost-effectiveness of BIM investment remains unclear. Some studies suggest a return on investment (ROI) as the result of cost-effective analysis calculations, which can be obtained by the introduction of BIM. However, a lack of research has led to inconsistent metrics being applied to the calculation of BIM-ROI for various types of projects. The purpose of this study is to develop a system to evaluate the performance of BIM using a K-means clustering algorithm and ROI analysis to reflect the cost-effectiveness of BIM investment. The proposed system also includes methods for determining best-case projects with high similarities from existing case projects and benchmarking their evaluation know-how, and its usability was verified through experienced BIM users.