DOI: 10.11908/j.issn.0253-374x.20019

作者:

作者单位: 1.南昌大学 建筑工程学院，江西南昌330031;2.江西省华赣环境集团有限公司，江西南昌330105


作者简介: 刘必君（1991—)，男，助理工程师，工学硕士，主要研究方向为工程项目管理。E-mail: liubijun2010@126.com


通讯作者: 叶雨辰（1991—)，女，助教，建筑学硕士，主要研究方向为建筑设计、绿色乡村建筑。E-mail: lilyye.yyc@qq.com

中图分类号: TU-9


基金项目: 江西省社会科学“十三五”规划（19YS38）




De-noising Auto-encoder-based Construction Cost Prediction
Author:

Affiliation: 1.School of Architecture and Engineering, Nanchang University, Nanchang330031, China;2.Jiangxi Province Huagan Environment Group Co. Ltd. , Nanchang330105,China


Fund Project:




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摘要:以高层建筑工程项目为例，对建筑工程施工成本影响因素进行可靠地识别和合理量化。基于深度学习下的栈式降噪自动编码器理论，结合神经网络，构建非线性工程项目的施工成本预测模型。通过实际案例在Matlab平台上进行仿真预测，实证了该方法在预测建筑工程施工成本上的可靠性和精确性。



Abstract: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.





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