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Open AccessArticle

Tunnel Surface Settlement Forecasting with Ensemble Learning

by Ke Yan 1,*, Yuting Dai 2, Meiling Xu 3 and Yuchang Mo 4
1
Department of Building, School of Design and Environment, National University of Singapore, Architecture Drive, Singapore 117566, Singapore
2
Zhejiang Geely Holding Group Co., LTD. 1760, Jiangling Road, Binjiang District, Hangzhou 310051, China
3
Nanhu College, Jiaxing University, Jiaxing 314001, China
4
Fujian Province University Key Laboratory of Computational Science, School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(1), 232; https://doi.org/10.3390/su12010232
Received: 7 December 2019 / Revised: 19 December 2019 / Accepted: 20 December 2019 / Published: 26 December 2019
(This article belongs to the Special Issue Smart Urban Planning and Land Management)
Ground surface settlement forecasting in the process of tunnel construction is one of the most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It is evident that modern artificial intelligence (AI) models, such as artificial neural network, extreme learning machine, and support vector regression, are capable of providing reliable forecasting results for tunnel surface settlement. However, two limitations exist for the current forecasting techniques. First, the data provided by the construction company are usually univariate (i.e., containing only the settlement data). Second, the demand of tunnel surface settlement is immediate after the construction process begins. The number of training data samples is limited. Targeting at the above two limitations, in this study, a novel ensemble machine learning model is proposed to forecast tunnel surface settlement using univariate short period of real-world tunnel settlement data. The proposed Adaboost.RT framework fully utilizes existing data points with three base machine learning models and iteratively updates hyperparameters using current surface point locations. Experimental results show that compared with existing machine learning techniques and algorithms, the proposed ensemble learning method provides a higher prediction accuracy with acceptable computational efficiency. View Full-Text
Keywords: tunnel settlement; time series analysis; ensemble learning; Adaboost.RT algorithm tunnel settlement; time series analysis; ensemble learning; Adaboost.RT algorithm
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Yan, K.; Dai, Y.; Xu, M.; Mo, Y. Tunnel Surface Settlement Forecasting with Ensemble Learning. Sustainability 2020, 12, 232.

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