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.

隧道地质条件的预测在地下工程中起着重要的作用，例如隧道的建设和隧道的动力设计。然而，由于地下地质条件的隐蔽性，在地质预测模型的设计中仍然存在许多挑战。在本文中，我们提出了一种用于地质预测的生成对抗网络（GAN-GP），可以根据从TBM配备的传感器收集的运行数据，准确估算隧道掘进机（TBM）施工隧道中每种岩土类型的厚度。GAN-GP的生成器包含功能提取（FE）和功能集成（FI）模块。前者从TBM运营数据中提取重要特征，而后者则提供地质条件预测，可以估算某个位置每种岩石土壤的厚度。GAN-GP的鉴别器确定FI模块的输出是否为真实的地质数据。在对抗训练之后，如果训练有素的鉴别器无法区分它们，则FI模块的输出将准确地近似真实的地质条件。实验结果证明了所提出的GAN-GP模型在地质预测中的有效性，并表明它优于最新的模型，包括支持向量回归（SVR），前馈神经网络（FNN）和随机森林（ RF）模型。FI模块的输出将准确地逼近真实的地质条件。实验结果证明了所提出的GAN-GP模型在地质预测中的有效性，并表明它优于最新的模型，包括支持向量回归（SVR），前馈神经网络（FNN）和随机森林（ RF）模型。FI模块的输出将准确地逼近真实的地质条件。实验结果证明了所提出的GAN-GP模型在地质预测中的有效性，并表明它优于最新的模型，包括支持向量回归（SVR），前馈神经网络（FNN）和随机森林（ RF）模型。