Affiliations: Department of Construction Management, Chengdu University of Technology, Chengdu, China
Correspondence:
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Corresponding author. Jing Dai, Department of Construction Management, Chengdu University of Technology, Chengdu, China. E-mail: [email protected].
Abstract: With the continuous improvement of the social and economic level, the investment in fixed assets in the whole society is increasing steadily, while the phenomenon of uncontrollable investment is becoming more and more serious. Therefore, it is very important to increase the investment estimate in the early stage of the project construction. Based on this, in this paper, by studying the BP neural network, a mathematical model of the prediction of engineering cost based on the improved BP neural network model was proposed; then, taking a 15-storey tall building in a residential district as a prediction object, by collecting and sorting out engineering cost data similar to the predicted object, the improved BP neural network model was estimated and trained; finally, the prediction of the engineering cost data for the project was carried out, and the actual results were compared with the estimation results of the traditional prediction model; thus, the speediness and accuracy of the proposed improved BP neural network model in the field of the prediction of engineering cost were verified.
Keywords: BP neural network, improved BP algorithm, engineering cost, prediction model
