摘要
研究目的:本文以张家庄大桥为例,进行关于桥梁工程运维成本三维非线性智能控制实证研究,将三维非线性智能控制技术运用到实际工程中,以提高运维管理的及时性和高效性,有效控制桥梁运维成本。研究结论:(1)本文在收集已完类似工程安全数据资料基础上,采用粒子群优化算法、BP神经网络等人工智能方法建立张家庄大桥运营维护成本预测模型,该模型可以通过输入目标工程的特征值,较准确预测出目标工程的运营维护成本;(2)运用PDCA、EVM、BIM等管理理论与方法加强桥梁运维全过程可视化控制,通过实际数据验证了运维成本管理的效果,降低了运维成本;(3)本文三维非线性智能控制模型可为桥梁工程运维成本控制提供借鉴和参考。
Research purposes: Taking Zhangjiazhuang bridge as an example,this paper makes an empirical study on the 3 D nonlinear intelligent control of bridge operation and maintenance cost,and applies the 3 D nonlinear intelligent control technology to the actual project to improve the timeliness and efficiency of operation and maintenance management,and effectively control the operation and maintenance cost.Research conclusions:( 1) On the basis of collecting safety data of similar projects,this paper uses artificial intelligence methods such as particle swarm optimization algorithm and BP neural network to establish the operation and maintenance cost prediction model of Zhangjiazhuang bridge. The model can accurately predict the operation and maintenance cost of the target project by inputting the characteristic value of the target project.( 2) PDCA,EVM,BIM and other management theories and methods are used to strengthen the visual control of the whole process of bridge operation and maintenance. Through the actual data,the effect of operation and maintenance management is verified,the operation and maintenance cost is reduced.( 3) In this paper,the 3 D nonlinear intelligent control model can provide reference for the operation and maintenance cost control of bridge engineering.
作者
段晓晨
喇海霞
胡天明
刘路海
王凯
DUAN Xiaochen;LA Haixia;HU Tianming;LIU Luhai;WANG Kai(Shijiazhuang Tiedao University,Shijiazhuang,Hebei 050043,China;Shijiazhuang Jiaojian Expressway Construction Management Co.Ltd,Shijiazhuang,Hebei 050000,China)
出处
《铁道工程学报》
EI
北大核心
2020年第9期102-107,共6页
Journal of Railway Engineering Society
基金
河北省交通厅科技项目计划(Y2011083)
河北省自然基金项目(G2019210226)。
关键词
桥梁工程
运营维护
成本管理
智能优化
bridge engineering
operation and maintenance
cost management
intelligent optimization