Expressway Project Cost Estimation With a Convolutional Neural Network Model
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 stag...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9279313/ |
| _version_ | 1798001984182157312 |
|---|---|
| author | Xiaojuan Xue Yuanhua Jia Yuanjie Tang |
| author_facet | Xiaojuan Xue Yuanhua Jia Yuanjie Tang |
| author_sort | Xiaojuan Xue |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T11:44:52Z |
| format | Article |
| id | doaj.art-da786687330e4f62a0393da37b1677a2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:44:52Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-da786687330e4f62a0393da37b1677a22022-12-22T04:25:38ZengIEEEIEEE Access2169-35362020-01-01821784821786610.1109/ACCESS.2020.30423299279313Expressway Project Cost Estimation With a Convolutional Neural Network ModelXiaojuan Xue0Yuanhua Jia1Yuanjie Tang2https://orcid.org/0000-0002-3625-1333School of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaWith 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.https://ieeexplore.ieee.org/document/9279313/Expressway project cost predictionconvolutional neural network modelartificial neural networkregression analysis |
| spellingShingle | Xiaojuan Xue Yuanhua Jia Yuanjie Tang Expressway Project Cost Estimation With a Convolutional Neural Network Model IEEE Access Expressway project cost prediction convolutional neural network model artificial neural network regression analysis |
| title | Expressway Project Cost Estimation With a Convolutional Neural Network Model |
| title_full | Expressway Project Cost Estimation With a Convolutional Neural Network Model |
| title_fullStr | Expressway Project Cost Estimation With a Convolutional Neural Network Model |
| title_full_unstemmed | Expressway Project Cost Estimation With a Convolutional Neural Network Model |
| title_short | Expressway Project Cost Estimation With a Convolutional Neural Network Model |
| title_sort | expressway project cost estimation with a convolutional neural network model |
| topic | Expressway project cost prediction convolutional neural network model artificial neural network regression analysis |
| url | https://ieeexplore.ieee.org/document/9279313/ |
| work_keys_str_mv | AT xiaojuanxue expresswayprojectcostestimationwithaconvolutionalneuralnetworkmodel AT yuanhuajia expresswayprojectcostestimationwithaconvolutionalneuralnetworkmodel AT yuanjietang expresswayprojectcostestimationwithaconvolutionalneuralnetworkmodel |