Prediction of Surface Settlement in Shield-Tunneling Construction Process Using PCA-PSO-RVM Machine Learning
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 3
Abstract
Surface settlement is one of the key engineering issues during shield construction process. In order to accurately predict surface settlement, this paper proposes a new machine learning method based on relevance vector machine (RVM), principal component analysis (PCA), and particle swarm optimization (PSO). Taking Beijing Metro Line 6 as a case study, the PCA-PSO-RVM model is used to make the prediction and compared with the prediction results of the RVM model using the same samples. In order to evaluate the reliability of the model, three evaluation indexes including mean relative error (MRE), root mean square error (RMSE), and Theil inequality coefficient (TIC) were calculated, and sensitivity analysis was carried out on them. The results show that the minimum relative error between PCA-PSO-RVM and the actual value is only 0.06%. The calculated MRE, RMSE, and TIC are 0.17%, 0.0714 mm, and 0.027%, respectively, which shows that PCA-PSO-RVM model has higher prediction accuracy, smaller deviations, and higher reliability compared with the three other models. Through sensitivity analysis, it is found that the weighted average internal friction angle () has the most significant impact on the surface settlement, which should be focused on in relevant research.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to acknowledge the financial support from the National Natural Science Foundation of China under Grant No. 52068016. The work in this paper was also supported by the Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering (Grant No. 19-Y-21-9, 20-Y-XT-01), the High Level Innovation Team and Outstanding Scholar Program of Universities in Guangxi Province (Grant No. 202006), and the Guangxi Natural Science Foundation under Grant Nos. 2020GXNSFAA297118 and 2020GXNSFAA159125.
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Published In
Journal of Performance of Constructed Facilities
Volume 37 • Issue 3 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 23, 2022
Accepted: Dec 29, 2022
Published online: Mar 2, 2023
Published in print: Jun 1, 2023
Discussion open until: Aug 2, 2023
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