Geological type recognition for shield machine using a semi-supervised variational auto-encoder-based adversarial method

doi:10.1016/j.tust.2024.106258

2025-02-01

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DOI：10.1016/j.tust.2024.106258

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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.

Keyword:

Geological type recognition

Shield machine

Enhanced Multi-Classification Adversarial

Generative Network

Variational Auto-Encoder

Adversarial training