Gray relational analysis and SBOA-BP for predicting settlement intervals of high-speed railway subgrade  

作　　者：Quanpeng He Shaoyuan Li 

机构地区：[1]School of Automatization and Electric Engineering,Lanzhou Jiaotong University,Lanzhou,China [2]School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai,China

出　　处：《Railway Sciences》2025年第2期199-212,共14页铁道科学(英文)

摘　　要：Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.

关 键 词：Gray relational analysis Secretary bird optimization algorithm Backpropagation neural network Subgrade settlement Interval prediction 

分 类 号：U21[交通运输工程—道路与铁道工程]