Work

Active learning for optimal feature combination selection in machine learning of TBM operational data

Year: 2026

Type: article

Abstract: Although extensive operational data from hard rock tunnel boring machines (TBMs) enable an extraction of numerous features for different prediction tasks using machine learning (ML), incorporating all TBM data features for ML may lead to high computation costs and potentially adverse model performance. This renders a significant challenge in ML of TBM data, i.e., how to determine an optimal combination of TBM features for improving model fidelity and efficiency. To address this challenge, this paper proposes an active learning method that intelligently selects superior feature combinations by evaluating performance of models developed from carefully selected subsets of features and adaptively changing the feature combinations. The proposed method is illustrated through a water conveyance tunnel project in China. It offers a robust framework for streamlining data processing while ensuring reliable predictions. In future studies, generalization capability of the proposed method will be further validated across more tunnel projects.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2026.106826

Authors: Chen-Hao Zhang, Yu Wang, Xu Li

Institutions: City University of Hong Kong, Hong Kong University of Science and Technology, Beijing Jiaotong University

Language: English

Topic: Tunneling and Rock Mechanics

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $5,490