上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 11: 1735 - 1744

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.109

船舶海洋与建筑工程

基于人工神经网络的深基坑支护结构侧移预测

  • 徐长节 ,
  • 李欣雨
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  • 1.浙江大学 滨海和城市岩土工程研究中心,杭州 310058
    2.浙江大学 平衡建筑研究中心, 杭州 310028
    3.华东交通大学 江西省岩土工程基础设施安全与控制重点实验室,南昌 330013
徐长节(1972—),教授,博士生导师,主要从事基坑工程支护结构方面的研究;E-mail:xucj@zju.edu.cn.

收稿日期: 2023-03-27

  修回日期: 2023-07-11

  录用日期: 2023-07-24

  网络出版日期: 2023-08-18

基金资助

国家自然科学基金(51878276);浙江省自然科学基金委员会-华东院联合基金(LHZ19E080001);浙江大学平衡建筑研究中心配套资金(20203512-10C)

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Lateral Deformation Prediction of Deep Foundation Retaining Structures Based on Artificial Neural Network

  • XU Changjie ,
  • LI Xinyu
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  • 1. Research Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, China
    2. Center for Balance Architecture, Zhejiang University, Hangzhou 310028, China
    3. Jiangxi Key Laboratory of Infrastructure Safety Control in Geotechnical Engineering, East China Jiaotong University, Nanchang 330013, China

Received date: 2023-03-27

  Revised date: 2023-07-11

  Accepted date: 2023-07-24

  Online published: 2023-08-18

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摘要

为了更精准地预测基坑开挖引起的支护结构侧移,采用支持向量机模型、传统人工神经网络模型及2种考虑时序性输入的循环神经网络模型,建立了不同基坑支护结构最大侧移、同一基坑不同工况支护结构侧移的预测模型.结果显示,人工神经网络可以根据工程实测数据实时更新和预测支护结构变形,有助于及时规划工程下一步施工工艺.在支护结构不同工况侧移的预测上,考虑了时序性输入的循环神经网络模型效果优于传统人工神经网络模型.

关键词： 基坑工程; 变形预测; 机器学习; 支护结构

本文引用格式

徐长节 , 李欣雨 . 基于人工神经网络的深基坑支护结构侧移预测[J]. 上海交通大学学报, 2024 , 58(11) : 1735 -1744 . DOI: 10.16183/j.cnki.jsjtu.2023.109

Abstract

In order to more accurately predict the lateral deformation of retaining structures caused by foundation pit excavation, this paper adopts support the vector machine model, traditional artificial neural network model, and two kinds of recurrent neural network models considering temporal inputs to establish a prediction model for the maximum lateral deformation of retaining structures in different foundation pits, and for the same foundation pit under different working conditions. The results show that the artificial neural network can update and predict the deformation of the retaining structure in real time based on the measured data of the project, which is helpful for timely planning of the next construction process of the project. In the prediction of lateral deformation of retaining structures under different working conditions, the cyclic neural network model considering temporal inputs is better than the traditional artificial neural network model.

Key words： excavation; deformation prediction; machine learning; retaining structures

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