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

doi:10.1007/s11709-019-0561-3

Front. Struct. Civ. Eng.

2019, Vol. 13

Issue (6) : 1363-1378 https://doi.org/10.1007/s11709-019-0561-3

RESEARCH ARTICLE

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

Renpeng CHEN^1,^2,³, Pin ZHANG³(

), Huaina WU^1,^2,³(

), Zhiteng WANG³, Zhiquan ZHONG⁴

¹. Key Laboratory of Building Safety and Energy Efficiency, Hunan University, Changsha 410082, China
². National Joint Research Center for Building Safety and Environment, Hunan University, Changsha 410082, China
³. College of Civil Engineering, Hunan University, Changsha 410082, China
⁴. China Construction Fifth Engineering Division Co., Ltd, Changsha 410082, China

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Abstract

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 (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.

Keywords EPB shield shield tunneling settlement prediction machine learning

Corresponding Author(s): Pin ZHANG,Huaina WU

Just Accepted Date: 19 July 2019 Online First Date: 11 September 2019 Issue Date: 21 November 2019

Cite this article:

Renpeng CHEN,Pin ZHANG,Huaina WU, et al. Prediction of shield tunneling-induced ground settlement using machine learning techniques[J]. Front. Struct. Civ. Eng., 2019, 13(6): 1363-1378.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-019-0561-3
https://academic.hep.com.cn/fsce/EN/Y2019/V13/I6/1363

Fig.1 Geological profile at the Changsha Metro Line 4 construction site.

Fig.2 Factors of tunneling-induced surface settlement.

Tab.1 Ranges of all variables for training set

Tab.2 Ranges of all variables for test set

Fig.3 Correlation matrix and scatter plot of all variables.

Fig.4 Marginal histogram of settlements predicted by multivariate regression method, compared to measured values.

Fig.5 Workflow of determining optimum ML algorithm.

Fig.6 Relation between the model performance and the parameter of test set. (a) BPNN; (b) WNN; (c) GRNN; (d) ELM; (e) SVM; (f) RF.

Tab.3 Values of optimum architectures or parameters in six ML algorithms

Fig.7 Predicted settlements using (a) BPNN, (b) WNN, (c) GRNN, (d) ELM, (e) SVM, (f) RF.

Fig.8 Box plot of the settlements predicted by ML methods, compare to measured settlements (a) training set and (b) test set.

Tab.4 Comparison of seven models for predicting settlements

Fig.9 Results of test set of different ML methods in predicting settlements.

Fig.10 Prediction errors of test set of different ML methods in predicting settlements.

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