Journal
SUSTAINABILITY
Volume 12, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/su12010232
Keywords
tunnel settlement; time series analysis; ensemble learning; Adaboost; RT algorithm
Funding
- National University of Singapore [R-296-000-208-133]
- research project on the 13th Five-Year Plan of higher education reform in Zhejiang Province [JG20180526]
- National Natural Science Foundation of China [61972156]
- Program for Innovative Research Team in Science and Technology in Fujian Province University
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available