Tunnel Surface Settlement Forecasting with Ensemble Learning

Ke Yan, Yuting Dai, Meiling Xu and Yuchang Mo
Additional contact information
Ke Yan: Department of Building, School of Design and Environment, National University of Singapore, Architecture Drive, Singapore 117566, Singapore
Yuting Dai: Zhejiang Geely Holding Group Co., LTD. 1760, Jiangling Road, Binjiang District, Hangzhou 310051, China
Meiling Xu: Nanhu College, Jiaxing University, Jiaxing 314001, China
Yuchang Mo: Fujian Province University Key Laboratory of Computational Science, School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China

Sustainability, 2019, vol. 12, issue 1, 1-11

Abstract: 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.

Keywords: tunnel settlement; time series analysis; ensemble learning; Adaboost.RT algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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