Handling missing data in large-scale TBM datasets: Methods, strategies, and applications

Substantial advancements have been achieved in Tunnel Boring Machine (TBM) technology and monitoring systems, yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results. This study aims to investigate the issue of missing data in extensive TBM datasets. Through a comprehensive literature review, we analyze the mechanism of missing TBM data and compare different imputation methods, including statistical analysis and machine learning algorithms. We also examine the impact of various missing patterns and rates on the efficacy of these methods. Finally, we propose a dynamic interpolation strategy tailored for TBM engineering sites. The research results show that K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms can achieve good interpolation results; As the missing rate increases, the interpolation effect of different methods will decrease; The interpolation effect of block missing is poor, followed by mixed missing, and the interpolation effect of sporadic missing is the best. On-site application results validate the proposed interpolation strategy's capability to achieve robust missing value interpolation effects, applicable in ML scenarios such as parameter optimization, attitude warning, and pressure prediction. These findings contribute to enhancing the efficiency of TBM missing data processing, offering more effective support for large-scale TBM monitoring datasets.