基于栈式降噪自动编码器的建筑工程施工成本预测

De-noising Auto-encoder-based Construction Cost Prediction

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.