Surface settlement prediction for urban tunneling using machine learning algorithms with Bayesian optimization

Dongku Kim, Kibeom Kwon, Khanh Pham, Ju Young Oh, Hangseok Choi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number104331
JournalAutomation in Construction
Volume140
DOIs
Publication statusPublished - 2022 Aug

Keywords

  • Bayesian optimization
  • K-fold cross-validation
  • Machine learning
  • Shield TBM
  • Surface settlement prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

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