City Research Online

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 an 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 and 0.027% respectively, which shows that PCA-PSO-RVM model has higher prediction accuracy, smaller deviations and higher reliability compared with other three models. Through sensitivity analysis, it is found that the weighted average internal friction angle.

Publication Type:	Article
Additional Information:	This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://ascelibrary.org/journal/jpcfev
Publisher Keywords:	hield tunneling; surface settlement; principal component analysis; particle swarm optimization; correlation vector machine; prediction model
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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