Expressway Project Cost Estimation With a Convolutional Neural Network Model

Abstract

With the development of the economy, the prediction of expressway project costs has gained increasing research attention. In this study, based on the convolution neural network (CNN) algorithm, the prediction of the expressway construction cost was analyzed with respect to the conceptual design stage. By summarizing the existing research results, 10 new factors related to the bridge and tunnel are creatively introduced into the cost-prediction index of the expressway conceptual design stage. In addition, the data structure of the expressway project cost prediction is defined and a CNN model is established. Finally, the project information of 415 expressways in China collected in this study is used to verify the research results. The results of the case analysis show that the 10 new indexes related to the bridge and tunnel can improve the prediction accuracy of the model. In addition, the CNN model is more suitable for solving the high-dimensional nonlinear problem of expressway cost prediction than the conventional artificial-neural-network and regression-analysis models, and it can improve the prediction accuracy. The findings of this study can be used to devise financial plans in the early stage of expressway construction and facilitate cost management at the conceptual design stage to help investors acquire project funds in advance.

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