Prediction and analysis of slurry shield TBM disc cutter wear and its application in cutter change time

Disc cutter is mainly used in cutting through hard rock. Disc cutter wear is inevitable during tunnel boring machine (TBM) tunneling, and excessive wear can lead to a decrease in tunneling efficiency and increase construction risks. Therefore, the importance of predicting the disc cutter wear accurately and arranging the cutter change plan can never be overemphasized. In this study, disc cutter wear data and replacement records for three shield chamber openings were analyzed, which were collected from a water conveyance tunnel project in Guangdong, China. It was found that the normal wear failure accounted for approximately 76.19%, while the abnormal wear failure accounted for 23.81%, and the range of tools to be replaced due to normal wear continues expanded as the tunneling distance increased. Based on the observed wear data, it is found that the wear rate of edge tools is much lower than that of center tools, which does not conform to the normal wear rules. Subsequently, a new disc cutter wear prediction model was developed considering energy conversion and uneven thrust distribution for explaining this problem. The determination method of the energy conversion coefficient k and thrust distribution coefficient b was proposed, and they had been obtained from the wear data in the first replacement. On the other hand, the proposed model has higher accuracy in both cutter wear and cutter change range prediction compared with the existing models. However, the prediction accuracy of the proposed model will decrease with the increase of the tunneling distance, so the undetermined coefficients k and b need to be updated in a timely manner.

Automated summary

This study analyzed disc cutter wear data and replacement records from a water conveyance tunnel project in Guangdong, China. It found that the wear rate of edge tools is much lower than that of center tools, which led to the development of a new disc cutter wear prediction model. The proposed model showed higher accuracy in predicting both cutter wear and change range compared to existing models.