A Kriging-Random Forest Hybrid Model for Real-time Ground Property
  Prediction during Earth Pressure Balance Shield Tunneling
Ziheng Geng Chao Zhang Yuhao Ren Minxiang Zhu Renpeng Chen Hongzhan Cheng
A kriging-random forest hybrid model is developed for real-time ground
property prediction ahead of the earth pressure balanced shield by integrating
Kriging extrapolation and random forest, which can guide shield operating
parameter selection thereby mitigate construction risks. The proposed KRF
algorithm synergizes two types of information: prior information and real-time
information. The previously predicted ground properties with EPB operating
parameters are extrapolated via the Kriging algorithm to provide prior
information for the prediction of currently being excavated ground properties.
The real-time information refers to the real-time operating parameters of the
EPB shield, which are input into random forest to provide a real-time
prediction of ground properties. The integration of these two predictions is
achieved by assigning weights to each prediction according to their
uncertainties, ensuring the prediction of KRF with minimum uncertainty. The
performance of the KRF algorithm is assessed via a case study of the Changsha
Metro Line 4 project. It reveals that the proposed KRF algorithm can predict
ground properties with an accuracy of 93%, overperforming the existing
algorithms of LightGBM, AdaBoost-CART, and DNN by 29%, 8%, and 12%,
respectively. Another dataset from Shenzhen Metro Line 13 project is utilized
to further evaluate the model generalization performance, revealing that the
model can transfer its learned knowledge from one region to another with an
accuracy of 89%.