铁道标准设计2017，Vol.61Issue(11)：76-81,6.DOI:10.13238/j.issn.1004-2954.2017.11.016
基于BP神经网络修正的自适应灰色模型的隧道变形预测研究
Research on Deformation Prediction of Tunnel Based on Adaptive Grey Model of BP Neural Network Correction
摘要
Abstract
The tunnel surrounding rock has the characteristics of high nonlinear deformation,which can be used to identify the development trend of tunnel deformation.In this paper,tunnel deformation is predicted preliminarily with adaptive GM (1,1) model,and the model parameters are optimized by particle swarm algorithm to ensure that the parameters of the adaptive model are the global optimal;secondly,the error correction model is established based on BP neural network to further improve the prediction accuracy.On this basis,the prediction model is applied to two engineering cases.The results show that the prediction model has better predication results in horizontal and vertical prediction with strong adaptive and recursive abilities.The predication results are proved in good agreement with the measurements with high accuracy to better reflect the deformation law of tunnel surrounding rock.The model can effectively conduct dynamic prediction of tunnel surrounding rock and can be used widely in tunnel deformation prediction.关键词
隧道/沉降变形/自适应GM(1,1)模型/BP神经网络/变形预测Key words
Tunnel/Settlement and deformation/Adaptive GM (1, 1) model/BP neural network/Deformation prediction分类
交通工程引用本文复制引用
叶超..基于BP神经网络修正的自适应灰色模型的隧道变形预测研究[J].铁道标准设计,2017,61(11):76-81,6.基金项目
陕西铁路工程职业技术学院科研项目(KY2016-02) （KY2016-02）
