期刊
FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING
卷 13, 期 6, 页码 1363-1378出版社
HIGHER EDUCATION PRESS
DOI: 10.1007/s11709-019-0561-3
关键词
EPB shield; shield tunneling; settlement prediction; machine learning
资金
- Research Program of Changsha Science and Technology Bureau [cskq1703051]
- National Natural Science Foundation of China [41472244, 51878267]
- Industrial Technology and Development Program of Zhongjian Tunnel Construction Co., Ltd. [17430102000417]
- Natural Science Foundation of Hunan Province, China [2019JJ30006]
Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors. This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namely, back-propagation neural network, wavelet neural network, general regression neural network (GRNN), extreme learning machine, support vector machine and random forest (RF), to predict tunneling-induced settlement. Field data sets including geological conditions, shield operational parameters, and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models. Three indicators, mean absolute error, root mean absolute error, and coefficient of determination the (R-2) are used to demonstrate the performance of each computational model. The results indicated that ML algorithms have great potential to predict tunneling-induced settlement, compared with the traditional multivariate linear regression method. GRNN and RF algorithms show the best performance among six ML algorithms, which accurately recognize the evolution of tunneling-induced settlement. The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据