Generative adversarial network for geological prediction based on TBM operational data. (1st January 2022)

• Record Type: Journal Article
• Authors: Zhang, Chao; Liang, Minming; Song, Xueguan; Liu, Lixue; Wang, Hao; Li, Wensheng; Shi, Maolin
• Is Part Of: Mechanical systems and signal processing. Volume 162(2022)
• Publication Date: 2022-01-01
• Subjects: Geological prediction -- Generative adversarial network -- Geological condition -- Feature extraction -- Tunnel boring machine; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108035
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The GAN-GP is designed for the geological prediction of TBM construction tunnel. Two specific tricks improve the GAN-GP's training stability and efficiency. The self-attention method extracts important features from TBM operational data. The GAN-GP outperforms the state-of-art models in the geological prediction task. Abstract: The prediction of tunnel geological conditions plays an important role in underground engineering, such as the tunnel construction and tunnel dynamic design. However, due to the invisibility of underground geological conditions, there remain many challenges in the design of geological prediction models. In this paper, we propose a generative adversarial network for geological prediction (GAN-GP) to accurately estimate the thickness of each rock-soil type in a tunnel boring machine (TBM) construction tunnel based on operational data collected from sensors equipped on the TBM. The generator of the GAN-GP contains feature-extraction (FE) and feature-integration (FI) modules. The former extracts the important features from the TBM operational data, and the latter produces the geological condition prediction, which estimates the thickness of each rock-soil type at a location. The discriminator of the GAN-GP determines whether the FI module's outputs are true geological data. After adversarial training, if the trained discriminator fails to distinguish them, the outputs of the FI module will accurately approximate the true geological condition. Experimental results support the effectiveness of the proposed GAN-GP model for geological prediction, and show that it outperforms the state-of-the-art models including support vector regression (SVR), feed-forward neural network (FNN) and random forest (RF) models.