Early prediction of the performance of green building projects using pre-project planning variables: data mining approaches

Abstract Early prediction of the success of green building projects is an important and challenging issue. The aim of this study was to develop a model to predict the cost and schedule performance of green building projects based on the level of definition during the pre-project planning phase. To this end, a three-step process was proposed: pre-processing, variable selection, and prediction model construction. Data from 53 certified green buildings were used to develop the models. After balancing the data set with respect to the proportion of cases in each of the outcome categories by pre-processing, the number of input variables was reduced from 64 to 13 and 7 for cost and schedule performance prediction respectively, using the ReliefF-W variable selection method. Then, cost and schedule performance prediction models were constructed using the selected variables and four different classifiers: a support vector machine (SVM), a back-propagation neural network (BPNN), a C4.5 decision tree algorithm (C4.5), and a logistic regression (LR). The classification performance of the four models was compared to assess their applicability. The SVM models exhibited the highest accuracy, sensitivity, and specificity in predicting both the cost and schedule performance of green building projects. The results of this study empirically validated that the cost and schedule performance of green building projects is highly dependent on the quality of definition in the pre-project planning phase.

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