In the process of tunneling, accurate and timely recognition of the geological type is significant to optimize the control parameters of the tunneling machine, improving tunneling efficiency and avoiding accidents. The shield machine operator in shield tunneling machine cannot directly observe the geological environment due to the closed working environment, so the soft method that can indirectly recognize the geological type by the machine parameters has become a research hotspot. However, most current soft methods use only a small amount of labeled data for supervised learning, and large amounts of unlabeled data is wasted. In order to use all data to improve the recognition performance of the classifier, a semi-supervised variational auto-encoder-based adversarial method (VAE-EMGAN) is proposed. Firstly, 50 parameters associated with geological types are selected and pre-processed, then the Variational Auto-Encoder (VAE) is trained by unlabeled data, and the generated part of VAE is added to the structure of Enhanced Multi-Classification Adversarial Generative Network (EMGAN) as a generator. Finally, the recognition accuracy of classifier is improved through adversarial training with labeled data, unlabeled data and generated data. We used data from upper and lower tunnels in Singapore to create two tasks to verify the validity and generalization performance of VEVE-EMGAN. The results show that the proposed model not only achieves high accuracy of all test sets on both tasks, but also has much better generalization performance than other models. Mean accuracy is 10.82%, 17.68%, 11.05%, 17.72%, 17.45%, 12.68% and 5.27% higher than SVM, KNN, RF, XGBoost, MLP, DNN and CNN respectively of test set 2 on task A; Mean accuracy is 13.06%, 12.80%, 7.64%, 18.31%, 8.74%, 7.94% and 4.05% higher than SVM, KNN, RF, XGBoost, MLP, DNN and CNN respectively of test set 2 on task B. In particular, the performance of the adversarial trained classifier is better than that has the same structure but separately trained classifier. Therefore, this method can use unlabeled data for adversarial training to improve the classification accuracy and generalization performance of the classifier, which has important implications for engineering practice.

中文翻译：

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基于半监督变分自编码器对抗法的盾构机地质类型识别


在掘进过程中，准确及时地识别地质类型对于优化掘进机的控制参数，提高掘进效率，避免事故发生具有重要意义。盾构掘进机中的盾构机操作人员由于工作环境封闭，无法直接观察地质环境，因此可以通过机器参数间接识别地质类型的软方法成为研究热点。然而，目前的大多数软方法只使用少量的标记数据进行监督学习，大量的未标记数据被浪费了。为了利用所有数据来提高分类器的识别性能，该文提出一种基于半监督变分自编码器的对抗方法（VAE-EMGAN）。首先，选择与地质类型相关的 50 个参数并进行预处理，然后用未标记的数据训练变分自动编码器 （VAE），并将 VAE 的生成部分作为生成器添加到增强型多分类对抗生成网络 （EMGAN） 的结构中。最后，通过使用标记数据、未标记数据和生成数据的对抗性训练来提高分类器的识别准确率。我们使用来自新加坡上下隧道的数据创建了两个任务来验证 VEVE-EMGAN 的有效性和泛化性能。结果表明，所提出的模型不仅在两个任务上都实现了所有测试集的高精度，而且比其他模型具有更好的泛化性能。平均准确率分别比任务 A 测试集 2 的 SVM、KNN、RF、XGBoost、MLP、DNN 和 CNN 高 10.82%、17.68%、11.05%、17.72%、17.45%、12.68% 和 5.27%;平均准确度为 13.06%、12.80%、7.64%、18.31%、8.74%、7。在任务 B 上，分别比测试集 2 的 SVM、KNN、RF、XGBoost、MLP、DNN 和 CNN 高 94% 和 4.05%。特别是，对抗性训练的分类器的性能优于具有相同结构但单独训练的分类器。因此，该方法可以使用未标记的数据进行对抗性训练，以提高分类器的分类准确性和泛化性能，这对工程实践具有重要意义。