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.
翻译:在隧道枯燥的地下机械(TBM)项目中,准确描述在隧道中分布的岩土类型可以降低建筑风险(例如地表住区和滑坡),提高建筑效率,在本文件中,我们提议根据TBM作业数据,为隧道地质重建建立一个积极的学习框架,称为AL-iGAN,这个框架包括两个主要部分:一个是使用积极学习技术,建议新的钻井地点,标注TBM作业数据,然后形成新的培训样品;另一个是地质重建递增的基因对抗网络(iGAN-GR),其重量可以逐步更新,通过使用新的样品改善重建业绩。