Journal
AUTOMATION IN CONSTRUCTION
Volume 27, Issue -, Pages 60-66Publisher
ELSEVIER
DOI: 10.1016/j.autcon.2012.05.013
Keywords
Cost performance prediction; Pre-project planning; Principal component analysis; Project success; Support vector regression model
Funding
- High-Tech Urban Development Program [07UrbanRenaissanceB03]
- Ministry of Land, Transport and Maritime Affairs of the Korean Government
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An accurate prediction of project performance in the pre-project planning stage - especially prediction of cost performance - is paramount to project stakeholders. The aim of this study is to propose and validate a hybrid predictive model for cost performance of commercial building projects using 64 variables related to the levels of definition in the pre-project planning stage. The proposed model integrates a support vector regression (SVR) model with principal component analysis (RCA). The proposed method was analyzed and validated based on 84 sets of data from an equal number of commercial building projects. Additionally, the result obtained using the proposed PCA-SVR model was compared with four other data-mining techniques. Experimental results revealed that the proposed PCA-SVR model is able to predict with high accuracy the cost performance of commercial building projects in the pre-project planning stage and is more efficient than the other four models. (C) 2012 Elsevier B.V. All rights reserved.
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