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(DESVR)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。
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.
作者
白祥瑞
戎晓力
文祝
张宁
BAI Xiangrui;RONG Xiaoli;WEN Zhu;ZHANG Ning(School of Science,Nanjing University of Science and Technology,Nanjing 210094,Jiangsu,China;School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,Jiangsu,China)
出处
《隧道建设（中英文）》
CSCD
北大核心
2021年第S02期336-345,共10页
Tunnel Construction
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
shield tunneling
surface settlement prediction
influence range
covariance matrix
support vector regression machine
