首页|Prediction of shield tunneling-induced ground settlement using machine learning techniques

Prediction of shield tunneling-induced ground settlement using machine learning techniques

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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 tunnelinginduced settlement.Field data sets including geological conditions,shield operational parameters,and tunnel geometry collected from four sections of tuunel 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 (R2) 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.

EPB shieldshield tunnelingsettlement predictionmachine learning

Renpeng CHEN、Pin ZHANG、Huaina WU、Zhiteng WANG、Zhiquan ZHONG

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Key Laboratory of Building Safety and Energy Efficiency, Hunan University, Changsha 410082, China

National Joint Research Center for Building Safety and Environment, Hunan University, Changsha 410082, China

College of Civil Engineering, Hunan University, Changsha 410082, China

China Construction Fifth Engineering Division Co., Ltd, Changsha 410082, China

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Research Program of Changsha Science and Technology BureauNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaIndustrial Technology and Development Program of Zhongjian Tunnel Construction Co.,LtdNatural Science Foundation of Hunan Province,China

cskq17030514147224451878267174301020004172019JJ30006

2019

结构与土木工程前沿
高等教育出版社

结构与土木工程前沿

CSTPCDCSCDSCIEI
影响因子：0.082
ISSN：2095-2430
年,卷(期)：2019.13(6)
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