摘要
针对铁路客站雨棚结构体系工程用量影响因素繁多复杂的问题,应用主成分分析法,分析得到影响其工程用量的主控因素,并作为BP神经网络的输入向量。然后,选取侧式站房钢桁架雨棚结构体系为估测研究对象,建立了基于主成分分析结果的BP神经网络估测模型。通过采用收集到的雨棚结构工程造价数据对估测模型进行验证,结果表明:应用主成分分析结果所建立的神经网络估测模型精度比较理想,误差符合要求,具有一定的实用价值。
Considering the complicated factors affecting railway passenger building canopy works,PCA(Principal Component Analysis) is used to identify the main controlling factors,which are taken as input vectors for neutral net.The study is then focused on the estimation of the structural system of steel truss canopy of lateral passenger building to establish BP neutral net estimation model on the basis of the analysis results of the main factors.The established model is examined with the collected data of canopy structural engineering cost,and the results show that the model so established demonstrates ideal accuracy with accepted tolerances and is practical in use.
出处
《铁道标准设计》
北大核心
2012年第5期157-160,共4页
Railway Standard Design
基金
2010年铁道部重点课题"铁路客站技术深化研究--铁路客站结构体系对工程投资影响研究"(2010G009-F)
关键词
哈大客运专线
铁路站房
工程量
估测
雨棚
主成分分析
BP神经网络
Harbin-Dalian Passenger Dedicated Line
Railway passenger building
Engineering works
Estimation
Canopy
PCA(Principal Component Analysis)
BP neutral net