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

TL;DR

This paper introduces AL-iGAN, an active learning framework utilizing incremental GANs to improve tunnel geological reconstruction accuracy from TBM operational data, reducing construction risks.

Contribution

The paper presents a novel active learning framework with an incremental GAN for enhanced geological reconstruction from TBM data, addressing data labeling and model updating challenges.

Findings

Effective in recommending drilling locations for data labeling

Improves reconstruction accuracy through incremental learning

Validated by numerical experiments

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.