Journal
AUTOMATION IN CONSTRUCTION
Volume 43, Issue -, Pages 23-29Publisher
ELSEVIER
DOI: 10.1016/j.autcon.2014.02.014
Keywords
Construction cost; Data mining; Text mining; Prediction
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This paper discusses how text describing a construction project can be combined with numerical data to produce a prediction of the level of cost overrun using data mining classification algorithms. Modeling results found that a stacking model that combined the results from several classifiers produced the best results. The stacking ensemble model had an average accuracy of 43.72% for five model runs. The model performed best in predicting projects completed with large cost overruns and projects near the original low bid amount. It was found that a stacking model that used only numerical data produced predictions with lower precision and recall. A potential application of this research is as an aid in budgeting sufficient funds to complete a construction project. Additionally, during the planning stages of a project the research can be used to identify a project that requires increased scrutiny during construction to avoid cost overruns. (C) 2014 Elsevier B.V. All rights reserved.
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