TY  - JOUR
AB  - 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.
VL  - 18
IS  - 3
SN  - 1726-0531
DO  - 10.1108/JEDT-08-2019-0195
UR  - https://doi.org/10.1108/JEDT-08-2019-0195
AU  - Jiang Qinghua
PY  - 2019
Y1  - 2019/01/01
TI  - Estimation of construction project building cost by back-propagation neural network
T2  - Journal of Engineering, Design and Technology
PB  - Emerald Publishing Limited
SP  - 601
EP  - 609
Y2  - 2024/04/20
ER  -