Journal Article

Prediction Model of Tunnel Boring Machine Disc Cutter Replacement Using Kernel Support Vector Machine

Zhang, Dailin, Liu, Yang, Wang, Di, Zhu, Guoli, Huang, Shuaiwen

2022

Overview

During tunneling processes, disc cutters of a tunnel boring machine (TBM) usually need to be frequently and unexpectedly replaced. Regular inspections are needed to check disc cutters’ status, which significantly reduces the work efficiency and increases the cost. This paper proposes a new prediction model based on TBM operational parameters and geological conditions that determines whether disc cutter replacement is needed. Firstly, an evaluation criterion for whether the cutters need to be replaced is constructed. Secondly, specific parameters related to the evaluation criterion are analyzed and 18 features are established on tunneling monitoring information. Then, the mapping model between the cutter replacement judgement and the established features is built based on a kernel support vector machine (KSVM). Finally, the data obtained from a Jilin water transport tunnel project is utilized to verify the performance of the proposed model. Test results show that the new model can obtain an average accuracy of 90.0% and an average F1 score of 86.2% on field data prediction based on data from past tunneling days. Therefore, the proposed data-predictive model can be used in tunneling to accurately predict whether disc cutters need to be replaced before human judgment, and thereby greatly improve tunneling safety and efficiency.

• Publisher: MDPI AG
• Subject: disc cutter / Drilling & boring machinery / Efficiency / Failure / Geology / Humidity / Inspections / machine learning / Methods / Neural networks / Sensors / support vector machine / Support vector machines / tunnel boring machine