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Predicting construction cost overruns using text mining, numerical data and ensemble classifiers
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DOI：10.1016/j.autcon.2014.02.014.png)
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Abstract
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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.
Keywords:
Construction cost
Data mining
Text mining
Prediction
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Automating hierarchical document classification for construction management information systems
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