论文标题
Al-Igan：基于TBM操作数据的隧道地质重建的主动学习框架
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data
论文作者
论文摘要
在隧道钻孔机（TBM）地下项目中，对隧道中分布的岩石类型的准确描述可以降低施工风险（例如{\ it，例如{\ it，例如}表面沉降和滑坡）并提高建筑效率。在本文中，我们提出了一个基于TBM操作数据的主动学习框架，称为Al-Igan，用于隧道地质重建。该框架包含两个主要部分：一种是使用主动学习技术的用法，用于推荐新的钻井位置以标记TBM操作数据，然后形成新的培训样本；另一个是地质重建（Igan-gr）的增量生成对抗网络，可以通过使用新样本来逐步更新其权重以提高重建性能。数值实验也验证了所提出的框架的有效性。
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.
