基于变分模态分解的CNN-LSTM模型在基坑变形预测中的应用  

APPLICATION OF THE VARIATIONAL MODE DECOMPOSITION-BASED CNN-LSTM MODEL IN PREDICTING EXCAVATION DEFORMATION

作　　者：方庆 陈胜 刘雪珠 邱伟 FANG Qing;CHEN Sheng;LIU Xuezhu;QIU Wei(Jiangsu Geotechnical Engineering Company,Nanjing 210000,China)

机构地区：[1]江苏省岩土工程公司,南京210000

出　　处：《力学与实践》2024年第5期1015-1022,共8页Mechanics in Engineering

基　　金：江苏省地质局基金项目(2020KY15)资助。

摘　　要：基坑变形会对工程造成许多不利影响,甚至可能引发土体塌陷和临近道路、建筑物开裂等灾害事故,所以基坑变形的预测是基坑工程中非常重要的一环。为了更准确地预测基坑变形,提出一种以监测数据的时间序列为输入的变分模态分解的卷积神经网络-长短期记忆(variational mode decompositionconvolutional neural network-long short term memory,VMD-CNN-LSTM)预测模型。基于南京江北新区图书馆基坑工程的现场监测数据,利用VMD-CNN-LSTM模型对CX07监测点的地下连续墙深层水平位移进行预测,得到的变形预测值与长短期记忆神经网络(long short term memory,LSTM)和卷积神经网络-长短期记忆(convolutional neural network-long short term memory,CNN-LSTM)模型的预测结果对比分析。可知VMD-CNN-LSTM模型相比其他两种模型具有更高的准确性。再选取另外两个监测点的监测数据对模型的预测效果进一步验证,证明了VMD-CNN-LSTM模型的适用性和稳定性。Excavation deformation can have numerous adverse effects on construction projects,potentially trigger catastrophic incidents such as soil collapse and cracking of nearby roads or buildings.Therefore,predicting excavation deformation is a crucial aspect of excavation engineering.To enhance the accuracy of these predictions,we propose a variational mode decomposition convolutional neural network-long short-term memory(VMD-CNN-LSTM)prediction model,which takes time series of monitoring data as input.Using onsite monitoring data from Nanjing Jiangbei new district library,the VMD-CNN-LSTM model was applied to forecast the deep horizontal displacement of the continuous underground wall at monitoring point CX07.A comparative analysis of the deformation predictions obtained from the LSTM and CNN-LSTM models demonstrates that the VMD-CNN-LSTM model offers superior accuracy.Further validation of the model's predictive performance was conducted using monitoring data from two other points,confirming the applicability and stability of the VMD-CNN-LSTM model.

关 键 词：基坑变形预测 变分模态分解 卷积神经网络 长短期记忆神经网络 

分 类 号：TU753[建筑科学—建筑技术科学]