摘要
为提高深基坑变形预测精度,在基坑地表沉降预测中引入反馈型Elman神经网络模型,利用Elman神经网络算法实现基坑沉降位移时间序列的滚动预测。以西安地铁5号线某车站基坑工程为例,基于组合预测思想,结合神经网络和马尔科夫链两种预测方法,建立了马尔科夫链优化的神经网络基坑地表沉降预测模型,借助马尔科夫链模型对其随机扰动误差进行修正,并与前馈型BP神经网络滚动预测模型对比。研究结果表明:Elman神经网络预测模型在修正前、后的预测效果均优于BP神经网络模型。设计开发出的基于MATLAB的图形用户界面(GUI)预测系统实现了模型预测过程便捷化,使预测过程能够以图形结果动态展现,具有较强实用价值。
In order to improve the accuracy of deep foundation pit deformation prediction,a feedback Elman neural network model was introduced into the foundation pit surface settlement prediction.The rolling prediction of foundation pit settlement displacement time series can be realized by using Elman neural network algorithm.Taking the foundation pit engineering of a station of Xi'an Metro Line 5 as the example,based on the idea of combined forecasting,combining the two forecasting methods of Elman neural network and Markov chain,the prediction model of ground subsidence of foundation pit was established.The random disturbance error was corrected by Markov chain model.The effect of Elman neural network prediction model before and after the modification were both better than the BP neural network prediction model.Graphical User Interface(GUI)prediction system based on MATLAB was designed and developed,making the model prediction process easy and convenient and the prediction process can be displayed dynamically by graphical results.
作者
贾哲
郭庆军
郝倩雯
JIA Zhe;GUO Qingjun;HAO Qianwen(School of Civil and Architecture Engineering,Xi′an Technological University,Xi′an 710021,China)
出处
《人民长江》
北大核心
2019年第1期202-206,219,共6页
Yangtze River
基金
国家自然科学基金项目(51374165)
教育部人文社会科学研究(17YJC630032)
关键词
基坑变形预测
神经网络
马尔科夫链
图形用户界面
foundation pit deformation prediction
neural network
Markov chain
graphical user interface
