岩土力学 ›› 2020, Vol. 41 ›› Issue (S1): 299-304.doi: 10.16285/j.rsm.2019.1653

• 岩土工程研究 • 上一篇    下一篇

基于小波智能模型的地铁车站基坑变形 时序预测分析

郭健1, 3, 陈健2,胡杨1   

  1. 1. 武汉轻工大学 土建学院,湖北 武汉 430023;2. 中国科学院武汉岩土力学研究所,湖北 武汉 430071; 3. 华中科技大学 土木工程与力学学院,湖北 武汉 430074
  • 收稿日期:2019-09-21 修回日期:2019-10-09 出版日期:2020-06-19 发布日期:2020-06-09
  • 通讯作者: 陈健,男,1972年生,博士,研究员,主要从事岩土工程随机场与地下空间开发研究。E-mail: jchen@whrsm.ac.cn E-mail: guojianxh@163.com
  • 作者简介:郭健,男,1968年生,博士(后),教授,主要从事岩土工程新技术的应用研究
  • 基金资助:
    中国工程院重点研究项目(No.2017XZ1205);湖北省技术创新重大项目(No.2017ACA186);宁波市公益类科技计划项目(No.2019c50012)

Time series prediction for deformation of the metro foundation pit based on wavelet intelligence model

GUO Jian1, 3, CHEN Jian2, HU Yang1   

  1. 1. School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan, Hubei 430023, China; 2. Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China; 3. Department of Controlled Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
  • Received:2019-09-21 Revised:2019-10-09 Online:2020-06-19 Published:2020-06-09
  • Supported by:
    This work was supported by the Major Research Project of the China Academy of Engineering Physics (2017XZ1205) , Hubei Technical Innovation Project (2017ACA186), and Ningbo Public Welfare Science and Technology Planning Project(2019c50012).

摘要: 软土地区的地铁车站基坑工程,因其地质条件和施工环境复杂,施工过程受到施工自身及诸多外界因素的干扰,其变形真实数据难以获取,无法准确判定基坑后期变形,施工事故风险控制难度大,极大影响着基坑工程自身及其周边环境安全。基于小波变换和Elman网络的数据分析与动态预测原理,构建一种小波智能的基坑变形时序预测模型(简称WEPM),应用于武汉地铁车站基坑工程预测,通过不断地对历史实测数据进行小波分解去噪,提取基坑变形真实数据,利用小波智能模型实现基坑变形的超前滚动式预测。分析结果表明,提出的小波网络预测模型具有泛化能力强、预测精度高的特点,能对不同条件下基坑变形进行时序预测分析,该模型可为未来武汉基坑工程的变形预测、施工安全与施工事故防范提供依据。

关键词: 地铁基坑, 变形预测, 小波变换, 时序分析

Abstract: The risk management of the construction on foundation pit of the metro in the soft clay area is challenging, not only affected by the complex geological conditions and construction environment, but also disturbed by engineering construction and external factors, making it difficult to obtain the actual data for predicting the deformation of foundation pit, thus resulting in the risk in construction and its surrounding environment. Based on the data analysis and the dynamic prediction theory of the wavelet transform and Elman neural network, a new time series wavelet Elman prediction model (WEPM) of the foundation pit deformation was proposed and was applied in the foundation pit engineering of the Wuhan metro. The actual deformation data was obtained based on the wavelet decomposition de-noising process of the previous measured data. Then the wavelet intelligence model was used to realize a rolling prediction on the deformation of foundation pit. The analysis results show that the WEPM has a high prediction accuracy and a high generalization performance, which was suitable for the time series prediction on the deformation of foundation pit under different conditions. The proposed model provides basis for deformation prediction, construction safety and accidents prevention of foundation pit construction in Wuhan.

Key words: foundation pit of metro, deformation prediction, wavelet transform, time series analysis

中图分类号: 

  • U452
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