基于小波变换的GASVM-ARMA模型在深基坑变形预测中的应用

牛帅星; 李庶林; 刘胤池; 安树正; 黄玉仁

doi:10.11835/j.issn.2096-6717.2021.092

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基于小波变换的GASVM-ARMA模型在深基坑变形预测中的应用

土木工程 | 更新时间：2023-05-25

- 基于小波变换的GASVM-ARMA模型在深基坑变形预测中的应用
- Application of GASVM-ARMA model based on wavelet transform in deformation prediction of deep foundation pit
- 土木与环境工程学报 2023年45卷第3期页码：16-23
- 作者机构：
  
  1.厦门大学建筑与土木工程学院，福建厦门 361005
  2.中铁二十四局集团福建公司，福州 350013
- 作者简介：
  
  牛帅星（1996- ），男，主要从事深基坑和抗浮结构研究，E-mail：shuaixing_niu@163.com。
  李庶林（通信作者），男，教授，E-mail：shulin.li@163.com。
- 基金信息：
  
  国家自然科学基金(51674218)
- DOI：10.11835/j.issn.2096-6717.2021.092
  中图分类号： TU433
- 收稿：2021-02-02，
  
  纸质出版：2023-06-25
- 稿件说明：
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牛帅星, 李庶林, 刘胤池, 等. 基于小波变换的GASVM-ARMA模型在深基坑变形预测中的应用[J]. 土木与环境工程学报, 2023,45(3):16-23.

NIU Shuaixing, LI Shulin, LIU Yinchi, et al. Application of GASVM-ARMA model based on wavelet transform in deformation prediction of deep foundation pit[J]. Journal of Civil and Environmental Engineering, 2023, 45(3): 16-23.
牛帅星, 李庶林, 刘胤池, 等. 基于小波变换的GASVM-ARMA模型在深基坑变形预测中的应用[J]. 土木与环境工程学报, 2023,45(3):16-23. DOI： 10.11835/j.issn.2096-6717.2021.092.

NIU Shuaixing, LI Shulin, LIU Yinchi, et al. Application of GASVM-ARMA model based on wavelet transform in deformation prediction of deep foundation pit[J]. Journal of Civil and Environmental Engineering, 2023, 45(3): 16-23. DOI： 10.11835/j.issn.2096-6717.2021.092.

摘要

为了提高深基坑施工过程中变形预测的准确度，提出一种基于小波变换分解与重构、采用遗传算法优化参数的支持向量机（GASVM）和自回归滑动平均（ARMA）模型相结合的组合模型预测方法。使用GASVM模型对小波分解后的趋势项进行一步预测和多步滚动预测，使用ARMA模型相应地对随机项进行预测，将预测值求和得到最终预测结果。以某地铁车站深基坑为案例，对3个监测点的支护桩深层水平位移进行预测分析，得到其一步预测的短期预测值和多步滚动预测的中长期预测值，并与单一采用GASVM模型得到的预测值进行对比。结果表明：组合模型有效减小了预测误差，在短期和中长期预测中均取得令人满意的结果。

Abstract

In order to improve the accuracy of deformation prediction during construction of deep foundation pits

this paper proposes a support vector machine with genetic algorithm optimized parameters (GASVM) and autoregressive moving average (ARMA) model based on wavelet transform decomposition and reconstruction. This paper uses GASVM model to make one-step prediction and multi-step rolling prediction for trend items after wavelet decomposition

utilizing ARMA model to predict random items accordingly

and to sum the predicted values to get the final prediction result. Finally

taking a deep foundation pit of a subway station as a case

the prediction and analysis of the deep horizontal displacement of the supporting piles at the three monitoring points are obtained

and the short-term prediction value of one-step prediction and the medium- and long-term prediction value of multi-step rolling prediction are obtained. The predicted value of the GASVM model is used for comparison. The results show that the combined model in this paper effectively reduces the predictive error

and has achieved satisfactory results in both short-term and medium- and long-term estimations.

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references

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