LAUSR

Abstract The benefits of adopting Building Information Modeling (BIM) in a construction project have been well recognized. However, it involves additional labor costs and the timeliness of task completion should be considered because a BIM application may not be required (and paid for) by the owner in the current stage of BIM development. At present, a project manager often faces much risk in making decisions about the cost because the BIM labor cost is supposed to be proportional to the gross (or total) floor area in practice, and the project managers can only adopt simple linear regression to estimate it. Although this method is straightforward, it tends to have a high risk of estimation error. Therefore, this research tried to develop a new methodology based on Cross Industry Standard Process for Data Mining (CRISP-DM) and proposed a hybrid approach by combining Random Forest (RF) and Simple Linear Regression (SLR) for improving the accuracy of prediction on a project's BIM labor cost in construction phase. Case studies are conducted to demonstrate and validate the prediction results using nineteen completed BIM projects from a leading construction company in Taiwan. A cost breakdown structure (CBS) was proposed to establish the training data set for machine learning. Moreover, this study proposes the use of effective floor area, instead of gross floor area, to be one of the features for training RF and SLR models. Clustering analysis is also adopted and confirmed to improve model performance. Through comparative studies, the hybrid approach of prediction methodology proposed in this study has been proved to be effective in reducing the risk of BIM labor cost prediction.