Designing the main drive motor capacity of Earth Pressure Balanced Tunnel Boring Machines (EPB TBMs) is a crucial task for every EPB tunnelling project. The machine needs to be equipped with sufficient power to master the geotechnical conditions of the respective project. On the other hand, overpowering the machine should be avoided for economic and sustainability reasons. Main drive torque estimation for EPB TBMs is challenging due to a multitude of impact factors and reciprocal mechanisms between the geotechnical conditions and the tunnelling process. In EPB TBM tunnelling active tunnel face support is achieved in soft and mixed ground or weak and unstable rock by generating a pressurized earth paste in the tool gap and excavation chamber of the machine. Complexity arises due to tribological and rheological effects of the active tunnel face support. These elements of uncertainty, the expected main drive torque is frequently overestimated to prevent a jamming of the machine in the ground. Mean main drive torque values often lie below 50 % of the installed nominal main drive torque capacity. In scope of this research machine learning algorithms, such as regressions, decision trees, tree ensembles, support vector machines and gaussian process regressions, have been used to predict the main drive torque. Models have been trained and tested on data collected from 9 different reference projects and validated on the data of 3 additional reference projects to test the transferability of the model. TBM diameters of the reference projects vary between 6,5 and 15,9 m and TBMs have been operating in a wide range of geotechnical boundary conditions. Different feature selection algorithms have been used and prediction results have been compared to models trained on manually selected features. Models using tree ensembles and manually selected features showed best prediction results and model performance. The machine learning approach returned a smaller and more accurate torque estimation range than traditional estimation approaches and prediction accuracy has been improved. Transparent and robust tree ensembles proofed to be suitable tools for TBM torque estimation.

中文翻译：

---

土压平衡隧道掘进中的预测机器学习用于隧道掘进机主驱动扭矩估计

设计土压平衡隧道掘进机 (EPB TBM) 的主驱动电机容量是每个 EPB 隧道项目的一项关键任务。机器需要配备足够的动力，以掌握相应项目的岩土条件。另一方面，出于经济和可持续发展的原因，应避免使机器过载。由于岩土条件和隧道开挖过程之间存在多种影响因素和相互作用机制，土压平衡盾构机的主驱动扭矩估算具有挑战性。在土压平衡盾构机隧道掘进中，通过在机器的刀具间隙和开挖室中产生加压土浆，在松软的混合地层或软弱且不稳定的岩石中实现主动隧道工作面支护。由于主动隧道掌子面支护的摩擦学和流变学效应而导致复杂性的增加。由于这些不确定因素，预期的主驱动扭矩经常被高估，以防止机器卡在地面上。平均主驱动扭矩值通常低于已安装标称主驱动扭矩容量的 50%。在本研究范围内，机器学习算法（例如回归、决策树、树集成、支持向量机和高斯过程回归）已用于预测主驱动扭矩。模型已根据从 9 个不同参考项目收集的数据进行了训练和测试，并在 3 个其他参考项目的数据上进行了验证，以测试模型的可迁移性。参考项目的 TBM 直径在 6.5 至 15.9 m 之间变化，并且 TBM 已在各种岩土边界条件下运行。使用了不同的特征选择算法，并将预测结果与手动选择的特征训练的模型进行了比较。使用树集成和手动选择特征的模型显示出最佳的预测结果和模型性能。机器学习方法返回的扭矩估计范围比传统估计方法更小、更准确，并且预测精度得到了提高。透明且强大的树集成被证明是 TBM 扭矩估计的合适工具。