Journal
AUTOMATION IN CONSTRUCTION
Volume 140, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.autcon.2022.104331
Keywords
Surface settlement prediction; Machine learning; Bayesian optimization; K-fold cross-validation; Shield TBM
Funding
- National Research Foundation of Korea (NRF) - Korea government (MOE) [2020R1A6A1A03045059]
- National R&D Project for Smart Construction Technology [22SMIP-A158708-03]
- Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure
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This paper uses machine learning algorithms to predict settlements induced by urban tunneling and enhances prediction performance by optimizing hyperparameters. The results show that the extreme gradient boosting algorithm has the highest prediction accuracy.
This paper describes the prediction of settlements induced by urban area tunneling using five machine learning (ML) algorithms. The settlement database, which was collected from a subway tunnel project in Hong Kong, consisted of 253 settlement measurements and 32 settlement influencing factors. The Bayesian optimizationbased hyperparameter tuning was applied to efficiently explore optimal combinations and to enhance prediction performance. The optimal hyperparameters were selected by considering the three-fold cross-validation (CV) result of training data. The performance of the developed model was evaluated by comparing the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values. The extreme gradient boosting algorithm demonstrated the highest prediction accuracy with RMSE, MAE, and R2 values of 1.606, 1.331, and 0.835, respectively.
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