A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China

Complex geological conditions and/or inappropriate shield tunnel boring machine (TBM) operation can significantly degrade both the excavation and safety of tunnel construction. In recent years, the excavation behaviour of shield TBMs has been a popular topic in the literature given the large volume...

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Veröffentlicht in:	Acta geotechnica Jg. 16; H. 12; S. 4061 - 4080
Hauptverfasser:	Bai, Xue-Dong, Cheng, Wen-Chieh, Li, Ge
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Boring machines Comparative analysis Comparative studies Complex Fluids and Microfluidics Data analysis Decision trees Drilling & boring machinery Earth pressure Engineering Excavation Feasibility studies Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Hydraulics Learning algorithms Learning behaviour Load distribution Machine learning Optimization Parameters Regression analysis Regression models Research Paper Sandy soils Screw conveyors Soft and Granular Matter Soil Soil Science & Conservation Solid Mechanics Support vector machines Torque Tunnel construction Tunneling Tunneling shields Tunnels Excavation behaviour Gradient boosting regression Shield tunnelling Machine learning Over-smoothing
ISSN:	1861-1125, 1861-1133
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Abstract	Complex geological conditions and/or inappropriate shield tunnel boring machine (TBM) operation can significantly degrade both the excavation and safety of tunnel construction. In recent years, the excavation behaviour of shield TBMs has been a popular topic in the literature given the large volume of data automatically collected by modern shields. These datasets provide an excellent opportunity to apply advanced data analysis techniques to improve predictions of shield tunnelling excavation behaviour. In this study, a framework to develop machine learning (ML)-based regression models for predicting the behaviour of an earth pressure balance (EPB) shield machine using tunnelling parameters is proposed. The feasibility of four ML algorithms, namely Linear Regression (LR), Decision Tree Regression (DTR), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR), to predict EPB shield excavation behaviour is explored through their application to a recent tunnelling case history in sandy soils. The results show that the misestimates were primarily attributed to a reduction of screw conveyor rotational speed (SCRS), induced by a lower injection volume, the artificial manipulation of penetration rate (PR), the local variations of total jacking load, and the use of ‘breakout’ cutterwheel torque (CT). The GBR model provided the best performance, while LR often performs the worst due to its inability to handle highly nonlinear relationships. DTR prevented the overfitting problem by using a lower max depth parameter towards sacrificing its accuracy. The performance of SVR was seriously affected by loss functions. The proposed optimisation scheme that prevents the over-smoothing problem during the STL decomposition elevates further the prediction accuracy.
AbstractList	Complex geological conditions and/or inappropriate shield tunnel boring machine (TBM) operation can significantly degrade both the excavation and safety of tunnel construction. In recent years, the excavation behaviour of shield TBMs has been a popular topic in the literature given the large volume of data automatically collected by modern shields. These datasets provide an excellent opportunity to apply advanced data analysis techniques to improve predictions of shield tunnelling excavation behaviour. In this study, a framework to develop machine learning (ML)-based regression models for predicting the behaviour of an earth pressure balance (EPB) shield machine using tunnelling parameters is proposed. The feasibility of four ML algorithms, namely Linear Regression (LR), Decision Tree Regression (DTR), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR), to predict EPB shield excavation behaviour is explored through their application to a recent tunnelling case history in sandy soils. The results show that the misestimates were primarily attributed to a reduction of screw conveyor rotational speed (SCRS), induced by a lower injection volume, the artificial manipulation of penetration rate (PR), the local variations of total jacking load, and the use of ‘breakout’ cutterwheel torque (CT). The GBR model provided the best performance, while LR often performs the worst due to its inability to handle highly nonlinear relationships. DTR prevented the overfitting problem by using a lower max depth parameter towards sacrificing its accuracy. The performance of SVR was seriously affected by loss functions. The proposed optimisation scheme that prevents the over-smoothing problem during the STL decomposition elevates further the prediction accuracy.
Author	Bai, Xue-Dong Cheng, Wen-Chieh Li, Ge
Author_xml	– sequence: 1 givenname: Xue-Dong surname: Bai fullname: Bai, Xue-Dong organization: School of Civil Engineering, Xi’an University of Architecture and Technology – sequence: 2 givenname: Wen-Chieh orcidid: 0000-0002-1902-7815 surname: Cheng fullname: Cheng, Wen-Chieh email: w-c.cheng@xauat.edu.cn organization: School of Civil Engineering, Xi’an University of Architecture and Technology, Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering (XAUAT) – sequence: 3 givenname: Ge surname: Li fullname: Li, Ge organization: School of Civil Engineering, Xi’an University of Architecture and Technology
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Keywords	Excavation behaviour Gradient boosting regression Shield tunnelling Machine learning Over-smoothing
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Snippet	Complex geological conditions and/or inappropriate shield tunnel boring machine (TBM) operation can significantly degrade both the excavation and safety of...
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SubjectTerms	Accuracy Algorithms Boring machines Comparative analysis Comparative studies Complex Fluids and Microfluidics Data analysis Decision trees Drilling & boring machinery Earth pressure Engineering Excavation Feasibility studies Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Hydraulics Learning algorithms Learning behaviour Load distribution Machine learning Optimization Parameters Regression analysis Regression models Research Paper Sandy soils Screw conveyors Soft and Granular Matter Soil Soil Science & Conservation Solid Mechanics Support vector machines Torque Tunnel construction Tunneling Tunneling shields Tunnels
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Title	A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China
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