## 5 Conclusions  
This paper presents a method to predict whether TBM disc cutters need replacement based on both operational and geological data. Through the study of historical disc cutter replacement data, this method can automatically predict whether a cutter replacement is needed after a current stroke, without installation of more sensors. This method also decreases the time consumed with regular manual inspections. Firstly, several methods were adopted to eliminate irrelevant data and improve the data quality effectively. Then, eight parameters were chosen from the processed tunneling data and 18 features were extracted to reflect time series information. Lastly, after comparing six different prediction algorithms, the proposed cost-sensitive Gaussian kernel support vector machine with an average accuracy of 98.1% and an average \(F_{1}\) score of 98.5% was selected as the prediction model.  
The test results on a Jilin's water transport tunnel dataset show that a 15-day training of the proposed model can provide 90.0% accuracy and 86.2% \(F_{1}\) score prediction performance on untrained data of the following two days. Moreover, the proposed model was tested on data from another project in Guangdong and acquired 88.8% accuracy and a 93.1% \(F_{1}\) score. The results show that the proposed cutter replacement prediction procedure could be applied in different tunneling projects, which can help with making disc cutter replacement plans. Therefore, our proposed method can reduce overall cutter inspection time and sensor cost usually required for disc cutter replacement. In addition, fewer manual inspections and on-time disc cutter replacements also mean higher tunneling safety.  
To further develop this study, it is very important to find a method to monitor geological changes online; more accurate rock properties information can thus be collected to train the proposed model. In addition, although 18 features were selected to build a reliable cutter replacements prediction model, other parameters could be tested for enhancing the overall prediction model's performance. For example, cutterhead structure data, such as cutter diameter, distances between cutters, and position radii may be used to generate higher prediction accuracy.  
Conceptualization, Y.L.; methodology, Y.L., G.Z. and D.Z.; validation, Y.L. and S.H.; formal analysis, Y.L., S.H. and D.W.; investigation, Y.L., S.H. and D.W.; data curation, S.H. and D.W.; writing--original draft preparation, Y.L. writing--review and editing, Y.L., S.H. and D.Z.; supervision, G.Z. and D.Z.; project administration, G.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2018 YFB1702504).  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/ChenZuyulWHR/YS-IWHR](https://github.com/ChenZuyulWHR/YS-IWHR)]([https://github.com/ChenZuyulWHR/YS-IWHR](https://github.com/ChenZuyulWHR/YS-IWHR)) (accessed on 16 February 2022).  
The authors would like to acknowledge the China Railway Engineering Equipment Group Co, Ltd. for the data provided.  
The authors declare no conflict of interest.