Surface settlement prediction for urban tunneling using machine learning algorithms with Bayesian optimization
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 optimization-based 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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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09265805
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Supplemental Notes:
- © 2022 Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
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Authors:
- Kim, Dongku
- Kwon, Kibeom
- Pham, Khanh
- Oh, Ju-Young
- Choi, Hangseok
- Publication Date: 2022-8
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 104331
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Serial:
- Automation in Construction
- Volume: 140
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0926-5805
- Serial URL: http://www.sciencedirect.com/science/journal/09265805
Subject/Index Terms
- TRT Terms: Bayes' theorem; Construction; Ground settlement; Machine learning; Mathematical prediction; Tunneling
- Subject Areas: Bridges and other structures; Construction; Railroads;
Filing Info
- Accession Number: 01847338
- Record Type: Publication
- Files: TRIS
- Created Date: May 26 2022 9:48AM