  In tunnel boring machine (TBM) underground projects, an accurate description
of the rock-soil types distributed in the tunnel can decrease the construction
risk ({\it e.g.} surface settlement and landslide) and improve the efficiency
of construction. In this paper, we propose an active learning framework, called
AL-iGAN, for tunnel geological reconstruction based on TBM operational data.
This framework contains two main parts: one is the usage of active learning
techniques for recommending new drilling locations to label the TBM operational
data and then to form new training samples; and the other is an incremental
generative adversarial network for geological reconstruction (iGAN-GR), whose
weights can be incrementally updated to improve the reconstruction performance
by using the new samples. The numerical experiment validate the effectiveness
of the proposed framework as well.
