基于DE-SVR的土压平衡盾构隧道施工阶段地表沉降预测研究

白祥瑞, 戎晓力, 文祝, 张宁

doi:10.3973/j.issn.2096-4498.2021.S2.043

隧道建设 >

2021 , Vol. 41 >Issue S2: 336 - 345

DOI: https://doi.org/10.3973/j.issn.2096-4498.2021.S2.043

研究与探索

基于DE-SVR的土压平衡盾构隧道施工阶段地表沉降预测研究

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（1. 南京理工大学理学院，江苏南京 210094； 2. 南京理工大学机械学院，江苏南京 210094）

白祥瑞(1995—)，女，安徽阜阳人，南京理工大学土木工程专业在读硕士，研究方向为数据挖掘在隧道施工中的应用研究。E-mail: rheawhite@njust.edu.cn。*通信作者：戎晓力， E-mail: rongxiaoli@njust.edu.cn。

网络出版日期: 2022-03-16

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Prediction of Surface Settlement in Construction Stage of Earth Pressure Balance Shield Tunnel Based on Differential Evolution AlgorithmSupport Vector Regression Machine

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(1. School of Science, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China; 2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China)

Online published: 2022-03-16

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摘要

为获取更加有效的地表沉降影响因素数据进行地表沉降的机器学习算法预测，基于差分进化算法优化支持向量回归机（DE-SVR）的机器学习方法，结合盾构法隧道施工阶段地表沉降的影响范围，以及该范围内地表沉降影响因素的多元时序数据特征，建立盾构法隧道施工阶段的地表沉降预测方法。以常州轨道交通1号线工程为例，结果表明，与传统的机器学习预测研究方法进行对比，该地表沉降预测方法具有更高的预测精度及更稳定的预测效果。

关键词： 盾构法施工; 地表沉降预测; 影响范围; 协方差矩阵; 支持向量回归机

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白祥瑞, 戎晓力, 文祝, 张宁 . 基于DE-SVR的土压平衡盾构隧道施工阶段地表沉降预测研究[J]. 隧道建设, 2021 , 41(S2) : 336 -345 . DOI: 10.3973/j.issn.2096-4498.2021.S2.043

Abstract

To obtain more effective factors affecting the surface settlement data for machine learning algorithm of surface settlement prediction, the support vector regression machine based on differential evolution algorithm (DESVR) of machine learning methods is studied to establish a surface settlement prediction method, combining with the influence range of surface settlement during shield tunnel construction stage, as well as multivariate time series data characteristics of surface subsidence influencing factors within the scope of the influence range. The prediction method of surface settlement is applied in Changzhou rail transit line 1 project, and the results show that the proposed method has higher prediction accuracy and more stable prediction effect compared to the traditional machine learning prediction method.

Key words： shield tunneling; surface settlement prediction; influence range; covariance matrix; support vector regression machine

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