基于ICEEMDAN-LSTM的地铁盾构隧道管片形变数据分析预测

Analyzing to Shield Tunnel Segments Deformation Data Based on ICEEMDAN-LSTM

地铁隧道安全监测以及监测数据的分析处理与预测是保障地铁隧道安全的重要手段。由于施工环境的影响,监测数据不可避免会含有噪声。本文以盾构地铁隧道管片变形自动监测数据为研究对象,提出基于ICEEMDAN-LSTM的变形监测数据分析预测方法。首先采用ICEEMDAN对监测数据进行分解处理,获得监测数据的IMF和残差分量;构建LSTM网络模型,应用LSTM模型对监测数据的IMF和残余分量进行预测,再对IMF和残余分量预测值进行叠加重构获得变形预测值。实验分析结果表明,ICEEMDAN-LSTM模型的预测精度明显高于BP、LSTM模型。

Measures of subway tunnel safety monitoring and monitoring data analysis and prediction are important means to ensure the safety of subway tunnel.Due to the influence of construction environment,there are noise in the monitoring data inevitably.Taking the automatic deformation monitoring data of shield subway tunnel segments as the research object,a deformation monitoring data analysis and prediction method was presented based on ICEEMDAN-LSTM.Firstly,ICEEMDAN was used to decompose the monitoring data and obtain the IMF and residual components of the monitoring data.The LSTM network model was built,and it was used to predict the IMF and residual components of the monitoring data.Finally,the predicted values of IMF and residual components were superimposed and reconstructed to obtain the predicted values of deformation.The experimental results show that ICEEMDAN-LSTM model has higher prediction accuracy than BP and LSTM model.