Sequential prediction of TBM penetration rate using a gradient boosted regression tree during tunneling
Hang-Lo Lee,Ki-Il Song,Chongchong Qi,Kyoung-Yul Kim
Abstract
Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R2 R2>0.79; whereas, a one-step prediction is R2
