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.
Bibliographical noteFunding Information:
This research was supported by the National R&D Project for Smart Construction Technology (No. 21SMIP-A158708- 02) funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure, and Transport , and managed by the Korea Expressway Corporation .
- 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