De-noising Auto-encoder-based Construction Cost Prediction

被引：0
作者：
Liu B. [1 ,2 ]
Ye Y. [1 ]
机构：
[1] School of Architecture and Engineering, Nanchang University, Nanchang
[2] Jiangxi Province Huagan Environment Group Co. Ltd., Nanchang
来源：
Tongji Daxue Xuebao/Journal of Tongji University | 2020年 / 48卷 / 06期
关键词：
Construction cost; Construction engineering; De-noising auto-encoder; Deep learning; Prediction;
D O I：
10.11908/j.issn.0253-374x.20019
中图分类号：
学科分类号：
摘要：
High-rise building projects being taken as the example, a study was made of the influencing factors about the construction cost for a reliable identification and reasonable quantification. On the basis of the theory of de-noising auto-encoder under deep learning as well as the neural network, a construction cost prediction model was established for nonlinear engineering projects. A case study was made of the model by a simulation prediction on the Matlab platform, which verified the proposed method for predicting the cost of engineering projects. © 2020, Editorial Department of Journal of Tongji University. All right reserved.
引用
收藏
页码：922 / 928
页数：6
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