Tunnel Boring Machine (TBM) construction, particularly with closed-face TBMs, faces uncertainties due to the inability of the operator to directly observe the ground ahead. These uncertainties can lead to time delays, cost overruns, and accidents. While supervised machine learning techniques have been used to predict geology from TBM sensor data, their performance drops significantly when applied to other projects, indicating poor generalization. To ensure accurate results and improved generalization to future data, supervised learning models require high-quality, well-labeled data which is not usually the case for TBM datasets. This paper addresses the issue of “noisy” labels in TBM datasets, which human operators and engineers often label with varying interpretations. A data-centric framework was adapted and applied to an Earth Pressure Balance Machines (EPBM) tunnel dataset to detect and identify these mislabeled datapoints. The framework's outputs were validated using two techniques and apply several methods to clean the dataset. The best-performing method was selected for the test set. The paper concludes by discussing the limitations of the proposed method, the challenges encountered, and future research directions in this area.

中文翻译：

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增强 TBM 隧道的地面分类模型：检测数据集中的标签错误

隧道掘进机 (TBM) 施工，尤其是封闭面 TBM，由于操作员无法直接观察前方地面而面临不确定性。这些不确定性可能导致时间延误、成本超支和事故。虽然监督机器学习技术已用于根据 TBM 传感器数据预测地质情况，但当应用于其他项目时，其性能显着下降，表明泛化能力较差。为了确保准确的结果并改进对未来数据的泛化，监督学习模型需要高质量、标记良好的数据，而 TBM 数据集通常并非如此。本文解决了 TBM 数据集中“嘈杂”标签的问题，人类操作员和工程师经常用不同的解释来标记这些标签。我们采用了以数据为中心的框架，并将其应用于土压平衡机 (EPBM) 隧道数据集，以检测和识别这些错误标记的数据点。该框架的输出使用两种技术进行了验证，并应用多种方法来清理数据集。为测试集选择了性能最佳的方法。本文最后讨论了该方法的局限性、遇到的挑战以及该领域未来的研究方向。