AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data

• URL: http://arxiv.org/abs/2212.00965v1
• Date: Fri, 2 Dec 2022 04:39:58 GMT
• Title: AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data
• Authors: Hao Wang, Lixue Liu, Xueguan Song, Chao Zhang, Dacheng Tao
• Abstract summary: In tunnel boring machines (TBM) underground projects, an accurate description of the rock-soil types distributed in the tunnel can decrease the construction risk. We propose an active learning framework, called AL-iGAN, for tunnel geological reconstruction based on TBM operational data.
• Score: 70.99694324397903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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