AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data
This work addresses tunnel construction safety and efficiency for engineering applications, representing an incremental improvement by integrating existing techniques in a new domain.
The paper tackles the problem of accurately reconstructing geological types in tunnel boring machine projects to reduce construction risks and improve efficiency, proposing an active learning framework (AL-iGAN) that combines active learning for data labeling and an incremental GAN for reconstruction, with numerical experiments validating its effectiveness.
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.