Abstract In tunneling, predictions of the rockmass conditions ahead of the face are of great interest to be able to take appropriate countermeasures at the right time. Besides investigations like drilling or geophysics, new approaches in mechanized tunneling aim at forecasting the geology ahead via Machine Learning models. These models are trained to forecast tunnel boring machine data by learning from recorded data in already excavated parts of the tunnel. Simply judging from high accuracies, these results may look promising at the first sight, but forecasts like this are mostly just delayed and slightly altered versions of the input data and no predictive value can result from them. This paper shows deficits in the current practice of data driven forecasts ahead of the tunnel face and raises impetus for further research in this particular field and TBM data analysis in general.

中文翻译：

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基于机器学习的TBM运行数据时滞预测的无意义

摘要 在隧道掘进中，工作面前方岩体状况的预测对于能够在适当的时候采取适当的对策具有重要意义。除了钻探或地球物理学等调查外，机械化隧道开挖的新方法旨在通过机器学习模型预测未来的地质情况。这些模型经过训练，可以通过从隧道已开挖部分的记录数据中学习来预测隧道掘进机数据。简单地从高准确度来看，这些结果乍一看可能很有希望，但像这样的预测大多只是输入数据的延迟和略有改动的版本，因此无法产生任何预测价值。