Stratigraphic heterogeneity inference under limited borehole data and massive tunneling data during shield tunneling construction: a semi-supervised learning approach

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It’s essential to identify the soil distribution for shield tunneling construction, especially in mixed ground conditions. Machine learning models have been conducted to infer the stratigraphic heterogeneity, but mostly relying on the geological survey report. The sparse borehole data has limited the identification accuracy of supervised learning models. Inspired by the perception that the shield tunneling data contains the geological information, a semi-supervised learning-based stratigraphic heterogeneity inference approach is presented in this study, which gets the utmost out of the limited borehole data and massive tunneling data. More specifically, a label propagation algorithm (LPA) soil identification model is developed. The rings with borehole data are defined as the labeled rings while other rings are unlabelled. The LPA is then employed to identify the soil distribution on the unlabelled rings with an iterative algorithm as the shield machine driving. A numerical experiment is conducted in Nanning metro line 1. The slurry pressured balanced shield was put forward in the mixed ground containing round gravel and mudstone. The field results show that the LPA approach can identify the mudstone distribution more accurately than traditional supervised learning methods. Input feature importance of tunneling data are also discussed