Estimation of construction project building cost by back-propagation neural network

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Citation

Jiang, Q. (2020), "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-08-2019-0195