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

A comparative study of different machine learning algorithms in predicting EPB shield behaviour:... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Geotechnica Springer Journals

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

Bai, Xue-Dong; Cheng, Wen-Chieh; Li, Ge
Acta Geotechnica , Volume 16 (12) – Dec 1, 2021
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References (63)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
ISSN
1861-1125
eISSN
1861-1133
DOI
10.1007/s11440-021-01383-7
Publisher site
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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.

Journal

Acta Geotechnica – Springer Journals

Published: Dec 1, 2021

Keywords: Excavation behaviour; Gradient boosting regression; Machine learning; Over-smoothing; Shield tunnelling

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