Classification of surface settlement levels induced by TBM driving in urban areas using random forest with data-driven feature selection

Dongku Kim, Khanh Pham, Ju Young Oh, Sun Jae Lee, Hangseok Choi

Research output: Contribution to journalArticlepeer-review

Abstract

Prediction of surface settlements induced by urban area tunneling is challenging owing to the unique tunneling conditions of tunnel sites. This study presents a machine learning (ML) framework to predict the surface settlement level using a data-driven feature selection method. A large-scale database consisting of 42 settlement-influencing factors and 253 settlement measurements was acquired from a subway tunnel project in Hong Kong. The feature selection approach with three evaluation indices, i.e., predictive power score, mutual information, and feature importance, navigated the relevant features within the database. The random forest algorithm was adopted to predict the four classes of settlements defined according to their settlement levels. The efficiency of the proposed feature selection approach was verified by the accuracy and F1 score, which increased by 14.5% and 15.4%, respectively. The proposed framework can enhance the applicability of ML approaches for predicting surface settlement at complex TBM tunneling sites.

Original languageEnglish
Article number104109
JournalAutomation in Construction
Volume135
DOIs
Publication statusPublished - 2022 Mar

Keywords

  • Classification
  • Feature importance
  • Feature selection
  • Machine learning
  • Mutual information
  • Predictive power score
  • Random forest
  • Shield TBM
  • Surface settlement prediction
  • Urban tunneling

ASJC Scopus subject areas

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

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