  Because of the fast advance rate and the improved personnel safety, tunnel
boring machines (TBMs) have been widely used in a variety of tunnel
construction projects. The dynamic modeling of TBM load parameters (including
torque, advance rate and thrust) plays an essential part in the design, safe
operation and fault prognostics of this complex engineering system. In this
paper, based on in-situ TBM operational data, we use the machine-learning (ML)
methods to build the real-time forecast models for TBM load parameters, which
can instantaneously provide the future values of the TBM load parameters as
long as the current data are collected. To decrease the model complexity and
improve the generalization, we also apply the least absolute shrinkage and
selection (Lasso) method to extract the essential features of the forecast
task. The experimental results show that the forecast models based on
deep-learning methods, {\it e.g.}, recurrent neural network and its variants,
outperform the ones based on the shallow-learning methods, {\it e.g.}, support
vector regression and random forest. Moreover, the Lasso-based feature
extraction significantly improves the performance of the resultant models.
