Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units.
This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples.
According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation.
The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.
Jiang, Q. (2019), "Estimation of construction project building cost by back-propagation neural network", Journal of Engineering, Design and Technology, Vol. 18 No. 3, pp. 601-609. https://doi.org/10.1108/JEDT-08-2019-0195Download as .RIS
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