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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Vydané v:Acta geotechnica Ročník 16; číslo 12; s. 4061 - 4080
Hlavní autori: Bai, Xue-Dong, Cheng, Wen-Chieh, Li, Ge
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021
Springer Nature B.V
Predmet:
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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Popis
Shrnutí: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.
Bibliografia:ObjectType-Case Study-2
SourceType-Scholarly Journals-1
content type line 14
ObjectType-Feature-4
ObjectType-Report-1
ObjectType-Article-3
ISSN:1861-1125
1861-1133
DOI:10.1007/s11440-021-01383-7