In hard rock TBM tunneling, the loss caused by disc cutter wear accounts for a large proportion of time and cost for the entire project. However, existing disc cutter wear prediction models mainly focus on predicting cutter consumption before construction and cannot predict the wear of each disc cutter. Moreover, the accurate rock parameters required in these models are challenging to obtain. Hence, these models are not capable of determining which cutter on cutterhead should be replaced during construction. To solve the problems mentioned above, this paper presents a novel field parameters-based method for estimating the wear of each disc cutter in real-time. The proposed method is implemented through the following steps. To begin with, a new health index is constructed and defined as the ratio of the rolling distance of a cutter in a small excavated section to its maximum rolling distance. Then, specific field parameters related to the new health index are analyzed and selected. Thereafter, the mapping model between the new health index and the specific field parameters is established based on a one-dimensional convolutional neural network. Finally, on the basis of the established model, the estimated health indices corresponding to all excavated sections of a disc cutter are accumulated to obtain its health status. The field data obtained from Mumbai metro tunnel was utilized to verify the effectiveness of the proposed method, which demonstrates that the proposed method can estimate the wear of each disc cutter in real-time with average accuracy as high as 87.8% on the test set. Therefore, the proposed method is capable of significantly reducing the time and cost of cutter inspection, replacement, and repair for TBM, thereby improve tunneling efficiency and reduce construction cost.

在硬岩TBM隧道中，由于盘式切割机磨损而造成的损失占整个项目时间和成本的很大一部分。然而，现有的盘式切割机磨损预测模型主要集中于在构造之前预测切割器消耗，并且不能预测每个盘式切割机的磨损。此外，这些模型中所需的准确岩石参数具有挑战性。因此，这些模型无法确定在施工期间应更换刀盘上的哪个刀具。为了解决上述问题，本文提出了一种基于现场参数的新方法，用于实时估计每个圆盘切刀的磨损。所提出的方法通过以下步骤实现。首先，构建新的健康指标并将其定义为小开挖部分中刀具的滚动距离与其最大滚动距离之比。然后，分析和选择与新健康指数相关的特定字段参数。此后，基于一维卷积神经网络，建立了新健康指数与特定领域参数之间的映射模型。最后，在建立的模型的基础上，累积与圆盘切割机所有开挖部分相对应的估计健康指标，以获得其健康状况。利用从孟买地铁隧道获得的现场数据验证了该方法的有效性，表明该方法可以实时估计每个圆盘切割机的磨损，平均精度高达87。测试集上的8％。因此，提出的方法能够显着减少TBM的刀具检查，更换和维修的时间和成本，从而提高了掘进效率并降低了施工成本。