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A Neural Network Model for Predicting Cost of Pre-Fabricated Housing
Abstract
Low performance of the construction industry stresses the need for improving current practices - especially in regard to cost. In this study the authors have found a critical set of variables for predicting total cost of pre-fabricated housing. A neural network model was applied on more than 30 projects. The model relies on 17 critical cost prediction variables. Verification, on 28 buildings, showed that: 85.7% of predicted values had the deviation lower 5%, while 10.7% had the deviation lower than 10%, in relation to the actual cost. After validating the model on new data the performances were as follows: 83.8% of predicted values had the deviation lower 5%, while 12.9% had the deviation lower than 10%. Thus, using this model, construction companies can influence project performance during project early phases, and acquire more competitive position on the market.
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