AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data
Abstract
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.
Cite
@article{arxiv.2212.00965,
title = {AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data},
author = {Hao Wang and Lixue Liu and Xueguan Song and Chao Zhang and Dacheng Tao},
journal= {arXiv preprint arXiv:2212.00965},
year = {2022}
}