Zhang Nan, Zhao Lin-Shuang
Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou 515063, China.
MethodsX. 2023 Jan 18;10:102017. doi: 10.1016/j.mex.2023.102017. eCollection 2023.
Cutter wear is one of the key factors influencing construction efficiency during shield tunnelling. Prediction of cutter wear can improve construction efficiency by reducing the times of cutter inspections in engineering practice. Evaluation of cutter life is vital for cutter wear prediction, however, existing cutter life indices can only estimate the health condition of all cutters on cutterhead on a holistic basis. A new index was proposed to evaluate cutter wear located at a specific installation position on cutterhead. A deep learning model integrating the index was developed for the estimation of accumulated cutter wear during real time shield tunnelling. The new index can be obtained by monitored field parameters and can predict cutter wear with historical wear patterns. The input and output data samples were reshaped for multi-step prediction. A shield tunnelling section in Guangzhou weathered granite was used for validation. The proposed method can help reduce the cost of cutter replacement by reducing the times of machine interventions. The method article is a companion paper to the original article [1].•Proposed index for prediction of cutter wear rate.•Deep learning model of 1D-CNN and GRU.•Multi-step cutter wear prediction.
刀具磨损是影响盾构隧道施工效率的关键因素之一。预测刀具磨损可通过减少工程实践中刀具检查次数来提高施工效率。刀具寿命评估对于刀具磨损预测至关重要，然而，现有的刀具寿命指标只能从整体上估计刀盘上所有刀具的健康状况。提出了一种新指标来评估位于刀盘特定安装位置的刀具磨损情况。开发了一个整合该指标的深度学习模型，用于实时盾构隧道施工过程中刀具累积磨损的估计。新指标可通过现场监测参数获得，并能根据历史磨损模式预测刀具磨损。对输入和输出数据样本进行了重塑以进行多步预测。利用广州风化花岗岩中的一段盾构隧道施工进行验证。所提出的方法可通过减少机器干预次数来帮助降低刀具更换成本。本文是原文章[1]的配套论文。•提出的刀具磨损率预测指标。•一维卷积神经网络（1D-CNN）和门控循环单元（GRU）的深度学习模型。•多步刀具磨损预测。
